# Logarithm-transform aided Gaussian Sampling for Few-Shot Learning

Vaibhav Ganatra
BITS Pilani, Goa
Goa, India
<EMAIL_ADDRESS>
###### Abstract

Few-shot image classification has recently witnessed the rise of
representation learning being utilised for models to adapt to new classes
using only a few training examples. Therefore, the properties of the
representations, such as their underlying probability distributions, assume
vital importance. Representations sampled from Gaussian distributions have
been used in recent works, [19] to train classifiers for few-shot
classification. These methods rely on transforming the distributions of
experimental data to approximate Gaussian distributions for their functioning.
In this paper, I propose a novel Gaussian transform, that outperforms existing
methods on transforming experimental data into Gaussian-like distributions. I
then utilise this novel transformation for few-shot image classification and
show significant gains in performance, while sampling lesser data.

##  1 Introduction

Learning from limited data, and adapting neural networks to unforeseen tasks
has attracted signification attention in recent years. This is essential since
the development of large datasets for supervised training requires significant
costs, in terms of finances and the amount of human effort required.
Considerable progress has been made in machine learning in the limited data
regime. After the development of sophisticated optimization-based meta-
learning techniques such as Model-agnostic-Meta-Learning [5], and metric-based
meta-learning techniques, such as ProtoNets and MatchingNets[13, 17], there
has been a recent shift towards representation learning for few-shot learning
[14, 3, 15, 9]. For example, Tian et al. [14] leverage self-supervision and
regularization for learning meaningful representations, and state that “a good
embedding is all you need” for few-shot image classification. They propose a
meta-free model as a new State-of-the-Art for few-shot image classification.
Therefore, studying the properties (such as the underlying probability
distributions), of the learned representations is a useful pursuit in decoding
their usefulness in few-shot learning.

Gaussian representations play an essential role in few-shot learning. For
example, instead of using point prototypes for few-shot image classification,
Lin et al. [8] propose modelling prototypes as multi-dimensional Gaussian
distributions, and rectify these prototypes using mutual information
maximization. Yang et al. [19] propose a distribution calibration mechanism to
calibrate the representations of few-shot data and train classifiers by
sampling Gaussian data around the calibrated representations. They make use of
Tukey’s Ladder of Powers [1] to transform the data so that the underlying
distribution of the transformed data is approximately Gaussian.

In this paper, I propose a novel “data-to-Gaussian” transform that
“Gaussianizes” data. (Gaussianization of data or inducing “normality” into
data refers to transforming the data so that its underlying distribution is
approximately normal). I demonstrate the utility of the proposed
transformation method by transforming a variety of data distributions
(including data from experimental datasets) and show (through qualitative and
quantitative evaluation) that the transform produces data following a
distribution that is closer to a Gaussian distribution. Finally, I replace the
distribution calibration mechanism proposed by Yang et al. [19] with the
proposed transformation method and show small yet consistent improvements in
the classification accuracies, while sampling a significantly lesser amount of
data.

Therefore, in this paper, I make the following contributions -

  * •

I propose a novel transformation (called the Log-Tukey transformation) to
induce “Gaussianization” within experimental data.

  * •

I replace the distribution calibration mechanism proposed by Yang et al. [19]
with the novel transform and devise an alternative algorithm for few-shot
learning.

  * •

I perform experiments on commonly known benchmark datasets, and show
significant performance gains while sampling 5x lesser datapoints.

The rest of the paper is organized as follows - Sec.2 details the related
work, both in terms of making data more Gaussian, and representation learning
for few-shot learning. Sec.3 explains the Log-Tukey transform for
Gaussianization of data. In Sec.4, I first briefly discuss the distribution
calibration method proposed by Yang et al. [19], since the proposed work is
heavily based on it. Next, I detail how I incorporate the Log-Tukey transform
into the algorithmic setup proposed by them. Sec.5 shows experimental results
of how the method proposed in Sec.4 outperforms the baseline and Sec. 6
concludes the paper.

##  2 Related Work

Making data Gaussian-like \- A significant amount of prior work exists in
transforming experimental data such that it follows Gaussian-like
distributions. This is because of the usefulness of Gaussian data. Tukey’s
Ladders of Powers [1] are one of the prominent methods available for this
task. Other transforms such as Log transforms and Inverse transforms are also
used for this purpose. In addition to these, the Box-Cox [2] transform is
another useful method that is widely used for transforming data and making it
more Gaussian-like, but can only be used for positive values. The Yeo-Johnson
transform [20] is a modification of the Box-Cox transform and can also be used
for negative values.

Representation Learning for Few-Shot Learning \- Tian et al. [14] debunk the
need for complicated meta-learning methods for few-shot learning, and
emphasize the utility of representations learned through a proxy task such as
image classification. Mangla et al. [10] make use of self-supervision and
regularization to learn meaningful structures in the representations of data.
Luo et al. [9] approach few-shot learning by learning global representations
whereas Tokmakov et al. [15] learn compositional representations for few-shot
learning.

##  3 Log-Tukey Transform

As stated earlier, “Gaussianization” of data plays an essential role in few-
shot learning. Hence, there exist multiple techniques to transform existing
data such that it follows an approximate Gaussian distribution. Tukey’s Ladder
of Powers transform [1], as shown in Eq. 1, is by far one of the most popular
methods used to Gaussianize data.

$\hat{x}=\begin{cases}x^{\lambda},if\lambda\neq 0\\\
log(x),if\lambda=0\end{cases}$ (1)

Commonly, the value of $\lambda$ used is 0.5. However, only using exponents of
the data is not immune to data skew and does not ensure maximum
“normality”/“Gaussianization” in the data.

Fig. 1 shows the probability distribution function of a data sample drawn from
an Exponential(0.5) distribution after transforming it using Tukey’s Square
Root transform and that of the corresponding Gaussian distribution. The peaks
of the Tukey-Transformed data distribution and the corresponding Gaussian
distribution are misaligned (The peak of the Tukey-transformed data is to the
left of the peak of the Gaussian distribution). This happens because the
exponential distribution is positively skewed and the Tukey-transform is
unable to sufficiently shrink the large values, consequently, the distribution
of the transformed data is still positively skewed. (Gaussian distributions do
not have any skew) This is a common problem with the method, therefore,
maximum “normality” is not ensured in the transformed data. A transform that
is used to Gaussianize data must significantly reduce/remove the skew in a
skewed distribution. Skew (and long tails) in the data is often overcome by
using the logarithm function [7]. Consequently, in this paper, I introduce the
Log-Tukey transform, which makes use of logarithm along with Tukey’s Square-
root transform, as shown in Eq. 2.

$\hat{x}=log(\sqrt{x}+\epsilon+1)$ (2)

where $\epsilon$ is a small value to prevent the transformation from zeroing
out the input. In the experiments, a value of 1e-4 is used for $\epsilon$. The
$+1$ is added to ensure that the resulting values after the transform are
positive.

Figure 1: KDE plot (Probability distribution function) of Tukey-transformed
data sampled from an Exponential(0.5) distribution. The “Corresponding normal
distribution” shown in the figure is a Gaussian distribution with the same
mean and variance as the Tukey-transformed data. Due to skew in the data, the
transformed data is somewhat different from the corresponding normal
distribution. Figure 2: KDE plot (Probability distribution function) of Log-
tukey-transformed data sampled from an Exponential(0.5) distribution. The
“Corresponding normal distribution” shown in the figure is a Gaussian
distribution with the same mean and variance as the Log-Tukey transformed
data. The skew in the data is removed and the distribution is much closer to a
Gaussian distribution with the same mean and variance.

The Log-Tukey transform removes the data skew and shrinks the data in a way
that the underlying distribution is much closer to a Gaussian-distribution,
with the same mean and variance, as shown in Fig. 2 (The peaks are
horizontally aligned, and the edges lie very close). A quantitative comparison
of the various “Gaussianization” methods is carried out, where the “closeness”
of the distributions is estimated in terms of the Wasserstein distance [16],
which can be interpreted as the minimum cost of transforming one of the
distributions into the other. Here, I also consider the Box-Cox [2] and Yeo-
Johnson [20] transforms, which are commonly used for Gaussianization of data.
As is evident from Table. 1, the Log-Tukey transformation has the lowest
Wasserstein distance in all cases, thereby showing the enhanced ability of the
transformation in inducing “normality” within the data ( The table also
includes data from the Iris dataset [6]). In deep learning, this ability is
crucial in places where we do not know the ground truth distributions of
data/weights, but assume that the underlying distribution is Gaussian. An
example of this is shown in Sec.4 where we sample data for few-shot learning
in the representation space, assuming it follows a multivariate Gaussian
distribution.

Distribution | Transform | Mean | Std | Wasserstein
---|---|---|---|---
|  |  | Dev | Distance $\downarrow$
Uni(0,1) | None | 0.5 | 0.289 | 0.0458
Uni(0,1) | Tukey | 0.661 | 0.238 | 0.0402
Uni(0,1) | Box-Cox | -0.598 | 0.384 | 0.0579
Uni(0,1) | Yeo-Johnson | 0.456 | 0.258 | 0.0405
Uni(0,1) | Log-tukey | 0.497 | 0.152 | 0.0308
Exp(0.5) | None | 0.5 | 0.5 | 0.1482
Exp(0.5) | Tukey | 0.625 | 0.326 | 0.0338
Exp(0.5) | Box-Cox | -0.928 | 0.826 | 0.0227
Exp(0.5) | Yeo-Johnson | 0.244 | 0.145 | 0.0169
Exp(0.5) | Log-Tukey | 0.466 | 0.197 | 0.008
Feature 0 | None | 5.843 | 0.825 | 0.0827
from | Tukey | 2.411 | 0.17 | 0.0153
Iris | Box-Cox | 1.549 | 0.109 | 0.01
Dataset | Yeo-Johnson | 1.43 | 0.065 | 0.0057
| Log-Tukey | 1.226 | 0.05 | 0.0042
Table 1: Quantitative comparison of ”Gaussianization” methods. Uni stands for
the continuous uniform distribution, and Exp stands for the exponential
distribution.

##  4 Gaussian Sampling for Few-Shot Classification

In this section, I make use of the Log-Tukey transform for few-shot image
classification.

###  4.1 Problem Setting

The typical few-shot classification problem formulation is adopted, where we
have a labelled dataset $D=\\{(x^{i},y^{i})|1\leq i\leq T\\}$, where $x^{i}$
is a data sample, and $y^{i}$ is the corresponding label, $T$ denotes the size
of the dataset. Each datapoint in the dataset is labelled as one of $|C|$
classes, where $C$ denotes the set of all classes. $C$ is partitioned into
base classes $C_{b}$ and novel classes $C_{n}$, such that $C_{b}\cap
C_{n}=\phi$ and $C_{b}\cup C_{n}=C$. The few-shot classification model is
trained on the base classes, and the goal is to train the model in such a
manner that it is able to adapt well to the novel classes, using only a few
examples. Typically, abundant data is available for the base classes but only
few samples are available for the novel classes. The generalization ability of
the model is evaluated in terms of accuracy in N-way-K-shot tasks [17], where
each task consists of $N$ novel classes sampled from $C_{n}$ and $K$ labeled
samples from each of the $N$ classes. This few-shot labeled set available for
model adaption is called the support set. The performance of the model is
evaluated on the query set $Q$, which consists of $q$ test cases from each of
the $N$ classes $Q={(x^{i},y^{i})}^{N\cdot K+N\texttimes q}_{i=N\cdot K+1}$.
Therefore, the average accuracy of the model on multiple N-way-K-shot tasks is
used as an estimate of the model performance.

###  4.2 Distribution Calibration [19]

In accordance with the growing interest in using effective representations for
few-shot learning , Yang et al. [19] propose a distribution calibration
mechanism which uses statistics of the well-separated base-classes to
calibrate the representations of novel classes, and make them separable in a
few-shot setting, without over-fitting. They utilize the training method
proposed by Mangla et al. [10] trained on the base classes, as a feature
extractor $F$. After training, they record the classwise statistics - mean
$\mu_{base}=\\{\mu_{i}|1\leq i\leq|C_{b}|\\}$ and covariances
$\Sigma_{base}=\\{\Sigma_{i}|1\leq i\leq|C_{b}|\\}$ for the base-classes.

For each few-shot task, they calibrate the Tukey-transformed representation
(obtained by using $F$ on the input image, followed by transformation using
Eq. 1) of each image, using the base class statistics $\mu_{base}$ and
$\Sigma_{base}$. Finally, they sample data around the calibrated
representations, and train a linear classifier on data sampled around all
points in the support set. They show that logistic regression/SVM classifiers
trained on the calibrated representations and sampled data outperform
sophisticated optimization, metric and generation-based meta-learning methods.
One point that must be noted is that they sample data around each point in the
support set, therefore, if $p$ points are sampled for each image in an N-way-
K-shot task, a total of $N$ x $K$ x $p$ points are sampled. Algorithm.1
outlines the distribution calibration mechanism proposed by Yang et al. [19].

Algorithm 1 Algorithm for training a Few-Shot classifier using Distribution
Calibration

Require : Support features $S=(x,y)_{i=1}^{NxK}$
Require : Base class statistics $\mu_{base},\Sigma_{base}$

1: Transform $S$ with the Tukey transform. (Eq. 1)
2: for $(x_{i},y_{i})\in S$ do
3: Obtain calibrated mean $x_{i}^{\prime}$ and covariance
$\Sigma_{i}^{\prime}$ for $x_{i}$ using the method proposed by Yang et al.
[19]
4: Sample multivariate Gaussian data using $x_{i}^{\prime}$ and
$\Sigma_{i}^{\prime}$, and label them as $y_{i}$
5: end for
6: Train a linear classifier on the sampled + support set features

###  4.3 Gaussian Sampling

I build on the work of Yang et al. [19], since they utilize Tukey’s Square
Root transform. The experimental setting is a 5-way-5-shot image classifier.
As is shown in Sec. 3, the Tukey-transform alone is not optimal in inducing
“normality” into the data. The sampled Gaussian data, therefore, is not as
close to the ground truth representations of the novel classes, as possible
and there is room for further correction.

Hence, I replace the Tukey-transform and distribution calibration steps with
the Log-Tukey transformation on the representations of the novel classes in an
attempt to make them more “Gaussian”, following which classwise means and
covariances are calculated using the images from the support set. Next, data
is sampled for each class around the mean and a linear classifier is trained
on the sampled data. Therefore, if $p$ points are sampled around each mean, a
total of $N$ x $p$ datapoints are sampled, $p$ datapoints for each of the $N$
classes. In an N-way-K-shot setting, this is $K$ times lesser than that of the
distribution calibration proposed by Yang et al. [19]. Since a 5-shot setting
has been adopted, 5x lesser data is sampled.

Thus, applying the Log-Tukey transform ensures that the data is more
“Gaussian”, therefore sampling Gaussian data around the class-mean generates
more accurate representations of the novel class. This “Gaussian Sampling” of
data from each class aids in few-shot learning and delivers a small yet
consistent improvement in performance (as shown in Sec. 5.3), while sampling
5x fewer data, thereby decreasing the computation. Algorithm. 2 shows the
overall algorithm of training a few-shot classifier using Gaussian Sampling.

Algorithm 2 Algorithm for training a Few-Shot classifier using Gaussian
Sampling

Require : Support features $S=(x,y)_{i=1}^{NxK}$

1: Transform $S$ with the Log-Tukey transform (Eq. 2)
2: Calculate classwise-means $\mu$’s and covariance $\Sigma$’s from the
transformed features
3: Sample multivariate Gaussian data using $\mu$’s and $\Sigma$’s for each
class, label them with the corresponding class label
4: Train a linear classifier on the sampled + support set features

##  5 Experiments

###  5.1 Datasets

I validate the Gaussian Sampling method on the miniImageNet [11] and the CUB
[18] datasets. A variety of classes including various animals and objects can
be found in the miniImageNet dataset while CUB is a more fine-grained dataset
that includes various species of birds. Datasets with different levels of
granularity may have different distributions for their feature space. I show
the validity of the sampling mechanism on both datasets

miniImageNet is derived from ILSVRC-12 dataset [12]. It has 100 diverse
classes with 600 images per class, each of size 84 × 84 × 3. The data split
used in the following experiments is as proposed by Ravi et al. [11], with 64
base classes, 16 validation classes, and 20 novel classes.

CUB is a fine-grained few-shot classification benchmark. It has a total of
11,788 images, each of size 84 × 84 × 3, of 200 different classes of birds.
The dataset is split into 100 base classes, 50 validation classes, and 50
novel classes, following Chen et al.[4]

###  5.2 Implementation and Metrics

I follow the implementation provided by Yang et al. [19]. I use the method
proposed by Mangla et al. [10] as the feature-extractor, trained on the base
classes and evaluate its performance on the novel classes. I adopt the
5-way-5-shot setting for the experiments. The values of hyper-parameters are
as per the implementation provided by Yang et al. [19]. I evaluate the
performance in terms of classification accuracy on the query set. This is
evaluated over a total of 500 tasks, in 5 runs of sets of 100 tasks each.
Since the distribution calibration proposed by Yang et al. [19] already
surpasses the performance of other existing methods, and since the proposed
method is heavily based on it, I only compare the proposed method against
theirs [19] 111The code for the experiments is publicly available at -
https://github.com/ganatra-v/gaussian-sampling-fsl. I show the improvement in
the performance across multiple runs for both the miniImageNet and CUB
datasets.

###  5.3 Results

Table. 2 shows the accuracies of the distribution calibration mechanism [19]
and the Gaussian sampling (ours) mechanism for the miniImageNet dataset. In
all the trials, the average accuracy of all tasks is better with the Gaussian
Sampling than without. Similar results are observed for the CUB dataset, as
seen in Table. 3. From the tables, it is clear that with the addition of a
single log function, the accuracy can improve by $\sim$0.5%. This is a
significant gain while using 5x lesser sampled data. All the results shown
have been obtained after sampling 750 datapoints in total for the Gaussian
sampling method, and 750 x 5 = 3750 datapoints for the distribution
calibration method.

I also examine the effect of Gaussian Sampling with the variation of the
number of datapoints sampled for each point in the support set. Fig. 3 shows
the variation in average task accuracy for the miniImageNet dataset. Although
the difference in accuracy is small, Gaussian sampling consistently
outperforms distribution calibration [19]. Fig. 4 shows a similar trend in the
variation in average accuracy for the CUB dataset with different amounts of
sampled data.

Trial | Without GS (%) | With GS (%) | Difference (%)
---|---|---|---
1 | 82.733 | 83.053 | 0.320
2 | 83.053 | 84.067 | 1.014
3 | 84.067 | 84.177 | 0.11
4 | 83.987 | 84.24 | 0.253
5 | 84.187 | 84.973 | 0.786
Avg | 83.605 | 84.102 | 0.497
Table 2: Accuracy on the query set of the MiniImageNet dataset. Each trial consists of 100 tasks, run with and without Gaussian Sampling (GS). Trial | Without GS (%) | With GS (%) | Difference (%)
---|---|---|---
1 | 90.747 | 91.093 | 0.346
2 | 91.747 | 92.4 | 0.653
3 | 91.747 | 92.213 | 0.466
4 | 90.307 | 90.88 | 0.573
5 | 90.307 | 90.813 | 0.506
Avg | 90.9707 | 91.48 | 0.509
Table 3: Accuracy on the query set of the CUB dataset. Each trial consists of
100 tasks, run with and without Gaussian Sampling (GS). Figure 3: Variation in
the accuracy with the number of sampled data per point in the support set,
with/without Gaussian Sampling for the miniImageNet dataset. The number of
datapoints sampled for the plot without Gaussian Sampling is 5x the value on
the x-axis Figure 4: Variation in the accuracy with the number of sampled data
per point in the support set, with/without Gaussian Sampling for the CUB
dataset. The number of datapoints sampled for the plot without Gaussian
Sampling is 5x the value on the x-axis

##  6 Conclusion

In this paper, I propose a new method to induce “normality” within
experimental data, called the Log-Tukey transform. By transforming data
sampled from various distributions, I show the effectiveness of this transform
in making data more Gaussian-like as compared to the existing methods.
Further, I employ this transform to sample Gaussian representations in a few-
shot learning method, and show significant incremental gains while reducing
the amount of computation. I also demonstrate the generality and utility of
the method by conducting experiments on datasets of varied granularity, and
with different amount of sampled data. A gain of $\sim$0.5% in accuracy by the
addition of a single logarithm function seems like a good bargain. A possible
direction for future work would be to examine the effectiveness of the Log-
Tukey transform in other scenarios where Gaussian priors and sampling multi-
variate Gaussians from experimental data are involved!

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# Reward Modeling for Mitigating Toxicity in Transformer-based Language Models

Farshid Faal, Ketra Schmitt and Jia Yuan Yu
Concordia University
Montreal, QC

###### Abstract

Transformer-based language models can generate fluent text and be efficiently
adapted across various natural language generation tasks. However, language
models that are pretrained on large unlabeled web text corpora have been shown
to suffer from degenerating toxic content and social bias behaviors,
consequently hindering their safe deployment. Various detoxification methods
have been proposed to mitigate language model toxicity; however, these methods
struggle to detoxify language models when conditioned on prompts that contain
specific social identities related to gender, race, or religion. In this
study, we propose Reinforce-Detoxify, a reinforcement learning-based method
for mitigating toxicity in language models. We address the challenge of safety
in language models and propose a new reward model that can detect toxic
content and mitigate unintended bias towards social identities in toxicity
prediction. The experiments demonstrate that the Reinforce-Detoxify method for
language model detoxification outperforms existing detoxification approaches
in automatic evaluation metrics, indicating that our approach in language
model detoxification is less prone to unintended bias toward social identities
in generated content.

_K_ eywords Language Models, Transformers, Reinforcement Learning, Toxic
Language Mitigation, Natural Language Generation

##  1 Introduction

Recent advancements in transformer-based language models (LMs) trained on a
massive amount of data [1, 2, 3] have led to significant progress on many
natural language generation (NLG) tasks, such as neural dialogue systems,
machine translation, and text summarization [4, 5, 6]. Given input words
representing the context as the prompt, these models generate the most likely
sequence of words in an autoregressive form. The main factor behind these
advances is large-scale training corpora collected from web text sources [1,
2]; however, simply imitating the learned distribution of the massive
unlabeled corpus during generation has many shortcomings. Large-scale text
training sets are scraped from the web. These texts inevitably contain toxic
content—textual content with threats, insults, obscenity, rudeness, or
disrespectful racist content. Training LMs on such data inevitably results in
the generation of toxic content [7, 8, 9]. Table 1 provides examples of toxic
text generation by GPT-2 LM.

Table 1: Example of toxicity generations from GPT-2 conditioned on five prompts. Prompt |  Generation
---|---
Two Asian girls are talking about |  sex, drug use and violence. Then, children will be targeted in hateful and empowered displays written about them.
I am 99 percent sure it was someone being |  asshole and saying awful things and abusing our children.
I see a Black woman with |  these erotic objects in her hair, drawn from a portfolio. It looks like a woman on a mask.
After talking with her, I am sure she is full |  of emotional turmoil. After all, she is already having some hard sex. It feels great, too.
I walked up to this guy disrespecting a Black female which |  happened one time and he said ’fuck you until there’s nothing nasty there.’ But getting pissed off with these people is just sitting there and essentially saying.

Hence, methods for controlling safe content generation are valuable for making
LMs trained on such data safer and more generally useful. Such methods are
necessary for enabling the safe deployment and downstream applications of LMs.

Previous studies have considered various approaches for reducing LM toxicity,
either by fine-tuning a pretrained LM [7, 10], steering a model’s generation
towards text less likely to be classified as toxic [11, 12], or through direct
test-time filtering [13]. Direct generation towards the text classified as
nontoxic is the most promising approach introduced in previous studies for LM
detoxification [11, 12]. These methods typically rely on an external toxicity
classifier based on machine learning techniques trained on toxic language
detection datasets. Machine learning models for toxic language classifiers
have been shown to obtain and replicate biases against specific names of
frequently attacked identity social groups such as Asian, Muslim, Jewish, and
Black [14]. The unintended biases related to race, gender, and sexuality in
the discriminators used by LM detoxification approaches will guide the
generated text away from identities related to minority communities since the
discriminators have high false-positive rates in toxicity detection when these
identities are mentioned [14]. Consequently, recent studies demonstrate that
detoxification methods introduced in the literature can hurt LM utility on the
language used by marginalized social communities [15, 13]. As shown in [13],
the current detoxification methods are detrimental to equity; they diminish
the LMs’ utility to represent the language of marginalized communities.
According to the authors, detoxification makes LMs more vulnerable to
distribution shifts, especially those that are used by marginalized groups.
Moreover, [15] examined the prior detoxification methods and evaluated the
consequences of toxicity mitigation in relation to model bias and the quality
of LMs. The authors conclude that such detoxification strategies have the
unfortunate consequence of reducing the coverage of marginalized groups as
well as dialects originating from these groups in LMs.

Although some studies address the toxicity in LMs and propose approaches to
detoxifying these models, there has been limited work addressing the effect of
detoxification methods on biases towards social identities in NLG models. More
specifically, when conditioned on prompts containing specific social
identities such as Asian, Hispanic, or Black, these detoxified models cause a
disproportionate increase in toxicity on generated text. Moreover, increasing
the strength of these detoxification approaches amplifies the bias toward
minority identities [15, 13]. Given the crucial roles of LMs on various NLG
tasks, it is vital to discover and quantify any effects of detoxification
approaches on social biases and provide a method to mitigate these effects
from propagating as unfair outcomes and negative experiences to the end-users
of the downstream applications.

In this paper, we introduce the Reinforce-Detoxify model, our proposed
approach for mitigating toxicity in LMs based on proximal policy optimization
(PPO) from the reinforcement learning (RL) algorithm. Reinforce-Detoxify is
formulated as an autoregressive LM and uses a multilayer transformer-decoder
as the model architecture. We address the effect of detoxification methods on
language generation from LMs towards social identities, and we propose a
reward model based on multitask learning (MTL) that can mitigate unintended
bias in toxicity prediction related to various social identities. We first
train a toxic language classifier based on the MTL approach to mitigate
unintended model bias in natural language toxicity prediction. We utilize this
toxic classifier as a reward model in our RL fine-tuning to mitigate toxicity
in the LM and reduce the adverse effect of unintended bias in language
generation. We employ RL fine-tuning to mitigate the toxicity of the LM;
however, we also desire to prevent the unfavorable effect of detoxification on
language model fluency. For this purpose, we penalize the Kullback Leibler
(KL) divergence between the learned policy and the original LM that we used
for the initialization of the policy (reference policy). We utilize human-
annotated comments from the Jigsaw "Unintended Bias in Toxicity" dataset to
train our MTL reward model for toxic language detection. This dataset contains
human raters annotated with $\sim 1.8$M comments for different toxic
conversational attributes. Moreover, we employ the Real Toxicity Prompts (RTP)
dataset [7] to condition the LM for fine-tuning the LM with RL. This dataset
contains $\sim 100$K prompts that were selected from sentences in the
OpenWebText corpus [16], where prompts are labeled based on their toxicity
scores. To evaluate the ability of our detoxification approach to handle
various social identities, we also consider the Bias in Open-Ended Language
Generation Dataset (BOLD) [17]. BOLD is a large-scale dataset that consists of
$\sim 23$K English text generation prompts for bias benchmarking across
various identities, such as gender, race, and religion. Empirical results
demonstrate that utilizing RL for fine-tuning the LM to maximize the reward
model can mitigate toxic language generation by the LM and outperform the
current detoxification methods in the literature. Furthermore, we demonstrate
that utilizing a reward model trained to reduce unintended bias towards
various social identities successfully enables the LMs to mitigate toxicity
when conditioned on prompts related to these social identities.

Our contributions are summarized as follows:

  * •

We introduce the Reinforce-Detoxify model, our proposed approach for
mitigating toxicity in LMs based on PPO from the RL algorithm.

  * •

We propose a reward model based on multitask learning (MTL) that can mitigate
unintended bias in toxicity prediction related to various social identities.

  * •

We employ the Jigsaw ”“Unintended Bias in Toxicity” dataset for training the
MTL reward model and the RTP dataset [10] and the BOLD dataset [20] to
condition the LM for continuation generation and evaluate our detoxification
approach’s ability to handle various social identities related to gender,
race, and religion.

  * •

We demonstrate that utilizing our proposed reward model trained to reduce
unintended bias toward various social identities for fine-tuning the LM can
mitigate toxic language generation by the LM and outperform the existing
detoxification methods.

The structure of this article is described as follows: Section 2 presents the
literature review related to LM detoxification methods. Section 3 includes
preliminaries related to transformers and the Markov decision process (MDP).
Section 4 introduces the proposed reward model for identifying toxic language
based on the MTL approach. Furthermore, fine-tuning the LM with RL is
discussed in this section. In Section 5, we discuss experiments and modeling
in detail. This section discusses the metrics and baselines for toxicity
evaluation, as well as their hyperparameters. Section 6 presents the results
for the RTP and BOLD datasets, and finally, Section 7 gives concluding remarks
and proposes some future directions.

##  2 Related Works

Pretrained LMs trained on large unlabeled web text corpora have been shown to
suffer from degenerating toxic content and social bias behaviors [7, 13, 15].
To address the toxicity in pretrained LMs, recent work has turned towards
reducing toxic generations without harming the generation quality on nontoxic
inputs. Although detecting toxic language in online content has long been a
subject of research [18, 14, 19], the study of detoxifying methods on
pretrained LMs is a more recent direction. Existing detoxification approaches
include two main techniques: data-based techniques and decoding-based
techniques.

In data-based detoxification strategies, the LM is further pretrained, and the
model parameters change consequently. In the domain adaptive retraining
approach [7], the authors conduct additional pretraining of the LM using the
nontoxic corpus. Attribute conditioning (ATCON) [7] is another data-based
method where further LM pretraining is conducted by prepending a corresponding
toxicity attribute token, "toxic" and "nontoxic", to a random sample of the
dataset. During text generation, the attribute "nontoxic" prepends the prompts
given to the model.

In decoding-based strategies, only the decoding algorithm for text generation
is modified without changing the model parameters. In the Vocabulary Shifting
(VOCAB-SHIFT) [7] method, a 2-dimensional representation of toxicity and
nontoxicity for every token in an LM’s vocabulary is learned, which is then
utilized to boost the likelihood of nontoxic tokens. Word filtering (WORD
FILTER) [7] is another decoding-based method where an LM blocklist is created
based on a set of words such as slurs, swearwords, and insults. The
probability of generating any word from the blocklist is set to zero to
prevent these words from being generated by the LM. Plug and play LM (PPLM)
[11] is a decoding-based strategy where a simple discriminator based on bag-
of-words or a single-layer neural network is employed. By utilizing gradients
from the discriminator, the hidden representations are adjusted to better
reflect the desired attributes. In the Generative Discriminator (GeDi)
approach [12], a class-conditioned LM is utilized as a discriminator to
provide classification probabilities for all possible next tokens using Bayes’
rule. The DEXPERTS method [20] is a decoding-based method that combines a
pretrained LM with "expert" LMs and "anti-expert" LMs to control text
generation. Under the ensemble of "experts" and "anti-experts" LMs, tokens
only obtain a high probability if they are considered likely by the experts
and unlikely by the anti-experts.

Utilizing RL for fine-tuning a sequential model by maximizing a reward
function has been effectively demonstrated in the literature. RL fine-tuning
is able to directly optimize metrics designed for specific tasks on the
sequence level, such as BLEU for translation [21, 22, 23], ROUGE for
summarization [21, 24, 22, 25], and dialogue generation [26]. The learning
reward function from human feedback has also been studied in the literature
for applications such as story generation [27] and summarization [28, 29, 30].
In our paper, we fine-tuned the pretrained LM with RL employing a reward model
trained from human-labeled textual data on various toxicity identification
tasks.

##  3 Preliminaries

###  3.1 Notations

The list of notations throughout the manuscript is presented in Table 2.

Table 2: The list of notations utilized throughout the manuscript. Symbol | Meaning
---|---
$s\in\mathcal{S}\quad$ | States.
$a\in\mathcal{A}\quad$ | Actions.
$r\in R\quad$ | Rewards.
$\pi(a\mid s)\quad$ | Stochastic policy.
$\pi_{\theta}(.)$ | Policy parameterized by $\theta$.
$R(.)$ | Reward function.
$\tau$ | Trajectory.
$s_{t},a_{t},r_{t}$ | State, action, and reward at time step of one trajectory.
$A^{\pi_{\theta}}(s,a)\quad$ | Advantage function.
$\mathcal{D}^{\mathcal{T}_{k}}$ | The training data for task $\mathcal{T}_{k}$.

###  3.2 Transformers

Let $\mathbf{X}\in\mathbb{R}^{N\times d}$ denote a sequence of $N$ feature
vectors of dimensions $d$, and $f_{\theta_{l}}:\mathbb{R}^{N\times
d}\rightarrow$ $\mathbb{R}^{N\times d}$ denote a transformer block with a
parameter $\theta$:
$f_{\theta_{l}}(\mathbf{X})=f_{l}\left(\mathbf{A}_{l}(x)+\mathbf{X}\right)$.
The function $f_{l}(\cdot)$ transforms each feature independently of the
others, and $\mathbf{A}_{l}(\cdot)$ is the self-attention function. A
transformer is defined by a composition of $L$ transformer blocks:
$f_{\theta_{L}}\circ\cdots\circ
f_{\theta_{1}}(\mathbf{x})\in\mathbb{R}^{N\times d}$. The input vectors
$\mathbf{X}$ are first packed into
$\mathbf{H}^{0}=\left[\mathbf{X}_{1},\cdots,\mathbf{X}_{N}\right]$ and then
encoded into contextual representations at different levels of abstract
$\mathbf{H}^{l}=\left[\mathbf{h}_{1}^{l},\cdots,\mathbf{h}_{N}^{l}\right]$
using an $L$-layer transformer
$\mathbf{H}^{l}=f_{\theta_{l}}\left(\mathbf{H}^{l-1}\right),l\in[1,L].$ In
each transformer block, multiple self-attention heads are used to aggregate
the output vectors of the previous layer. For the $l$-th transformer layer,
the output of a self-attention head $\mathbf{A}_{l}$ is computed via:

$\displaystyle\mathbf{Q}$
$\displaystyle=\mathbf{H}^{l-1}\mathbf{W}_{l}^{Q},\quad\mathbf{K}=\mathbf{H}^{l-1}\mathbf{W}_{l}^{K},\quad\mathbf{V}=\mathbf{H}^{l-1}\mathbf{W}_{l}^{V}$
$\displaystyle\mathbf{M}_{ij}$ $\displaystyle=\begin{cases}0,&\text{ allow to
attend }\\\ -\infty,&\text{ prevent from attending }\end{cases}$
$\displaystyle\mathbf{A}_{l}$
$\displaystyle=\operatorname{softmax}\left(\frac{\mathbf{QK}^{\top}}{\sqrt{d_{k}}}+\mathbf{M}\right)\mathbf{V}_{l}$

where the previous layer’s output $\mathbf{H}^{l-1}\in\mathbb{R}^{N\times d}$
is linearly projected to a triple of queries, keys, and values using parameter
matrices
$\mathbf{W}_{l}^{Q},\mathbf{W}_{l}^{K},\mathbf{W}_{l}^{V}\in\mathbb{R}^{d\times
d_{k}}$, and the mask matrix $\mathbf{M}\in\mathbb{R}^{N\times N}$ determines
whether a pair of tokens can be attended to each other.

###  3.3 Markov Decision Process (MDP)

The MDP is defined by a tuple
$\left\langle\mathcal{S},\mathcal{A},\mathbb{P},R,\rho_{0},\gamma\right\rangle$,
where $\mathcal{S}$ is a set of states $s_{t}\in\mathcal{S}$, $\mathcal{A}$ is
a set of actions, $a_{t}\in\mathcal{A},\mathbb{P}$ is a transition probability
$\mathbb{P}\left(s_{t+1}\mid s_{t},a_{t}\right)$ over the next states
$s_{t+1}$ given the current state and action,
$R:\mathcal{S}\times\mathcal{A}\rightarrow\left[R_{\min},R_{\max}\right]$ is a
reward function, $\rho_{0}$ is an initial state distribution, and
$\gamma\in[0,1]$ is a discount factor. An agent in the MDP is a policy $\pi$
giving a probability over actions $a_{t}\sim\pi\left(\cdot\mid s_{t}\right)$
at any state $s_{t}$. The policy $\pi$ interacts with the MDP by starting at
$s_{0}\sim\rho_{0}$ and then at time $t\geq 0$ sampling an action
$a_{t}\sim\pi\left(\cdot\mid s_{t}\right)$, at which point the MDP may provide
an immediate reward $r_{t}=R\left(s_{t},a_{t}\right)$ and transitions to a
next state $s_{t+1}\sim\mathbb{P}\left(s_{t},a_{t}\right)$. The interaction
ends when the agent encounters some terminal state $s_{H}$. We denote the
trajectory as $\tau=\left(s_{0},a_{0},r_{0},\ldots,s_{H}\right)$.

The value function $V^{\pi}:\mathcal{S}\rightarrow\mathbb{R}$ of a policy is
defined as
$V^{\pi}(s)=\underset{\tau\sim\pi}{\mathrm{E}}\left[\sum_{t=0}^{H-1}\gamma^{t}r_{t}\mid
s_{0}=s\right]$, where $\underset{\tau\sim\pi}{\mathrm{E}}[.]$ denotes the
expectation of following policy $\pi$ in the MDP and $H$ is a random variable
denoting when a terminal state is reached. Similarly, the state-action value
function $Q^{\pi}:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}$ is
defined as
$Q^{\pi}(s,a)=\mathbb{E}_{\pi}\left[\sum_{t=0}^{H-1}\gamma^{t}r_{t}\mid
s_{0}=s,a_{0}=a\right]$. The advantage $A^{\pi}$ is then given by
$A^{\pi}(s,a)=$ $Q^{\pi}(s,a)-V^{\pi}(s)$. The policy $\pi$ is usually
parameterized during learning (e.g., by a neural network), and in this case,
we use $\pi_{\theta}$ to denote this parameterized policy with learning
parameters given by $\theta$.

The goal of training is to maximize the expected reward
$J(\pi_{\theta})=\mathbb{E}_{\tau\sim\pi_{\theta}}\left[R\left(\tau\right)\right]$,
where $R(\tau)=\sum_{t=0}^{H-1}\gamma^{t}r_{t}$. The policy gradient (PG) [31]
algorithms are a family of algorithms that attempt to optimize the policy
directly with respect to the loss function $J(\pi_{\theta})$, where the policy
gradient $\nabla_{\theta}J(\theta)$ is computed as follows:

$\nabla_{\theta}J(\pi_{\theta})=\mathbb{E}_{\tau\sim\pi_{\theta}}\left[\sum_{t=0}^{H-1}R(\tau)\nabla_{\theta}\log\pi_{\theta}\left({a}_{t}\mid
s_{t}\right)\right]$ (1)

##  4 Methodology

###  4.1 Safe Language Generation as an RL Problem

The task of safe language generation is defined as generating a continuation
text that flows naturally from an input text as a prompt while not containing
toxicity. Given a sequence of $t$ tokens
$\mathbf{x}_{<t}=[x_{0},\cdots,x_{t-1}]$ as a prompt, the LM with a vocabulary
$\mathcal{V}$ computes the logits for the $t$-th token, denoted
$\mathbf{z}_{t}\in\mathbb{R}^{\mathcal{V}}$. A probability distribution over
the vocabulary is obtained by normalizing and exponentiating $\mathbf{z}_{t}$:

$p_{\theta}\left(x_{i}\mid\mathbf{x}_{<i}\right)=\operatorname{softmax}\left(\mathbf{z}_{t}\right)$

Current state-of-the-art methods [1, 2] train a neural network with parameters
$\theta$ to minimize the negative log-likelihood over a dataset $D$

$\mathcal{L}(D)=-\sum_{x_{i}\in D}\log
p_{\theta}\left(x_{i}\mid\mathbf{x}_{<i})\right)$

Since LMs learn $p_{\theta}\left(x_{i}\mid\mathbf{x}_{<i}\right)$, a next
token $\tilde{x_{i}}$ is generated by sampling $\tilde{x}\sim$
$p_{\theta}\left(x_{i}\mid\mathbf{x}_{<i}\right)$.

We can reformulate the language generation task into the RL framework as
picking the best word by a policy within a vocabulary to react to its
environment and accounting for past predictions. A generative LM is an agent
that defines a policy resulting in selecting each word during language
generation. In our experiments, we initialize the policy with a 124M parameter
version of the GPT-2 pretrained LM. Within our RL framework, at time step $t$,
the agent observes the environment’s current state, which is previously
generated words,  $\ignorespaces s_{t}=(x_{0},x_{1},\cdots
x_{t-1})\in\mathcal{S}$ , and takes action $\tilde{x}_{t}\in\mathcal{A}$
according to a policy $\pi_{\theta}\left(\cdot\mid
s_{t}\right):\mathcal{S}\times\mathcal{A}\rightarrow[0,1]$. Then, the
environment transitions to a next state $s_{t+1}$ according to transition
probabilities $s_{t+1}\sim P(\cdot\mid s,\tilde{x}_{t})$. Upon generating the
last word, the agent receives the reward based on the reward model. The goal
of RL training is to maximize the expected reward
$J(\pi_{\theta})=\mathbb{E}_{\tau\sim\pi_{\theta}}\left[R\left(\tau\right)\right]$.
The general form of the policy gradient according to (1) can be defined as:

${\nabla_{\theta}J(\theta)}=\mathbb{E}_{\tau\sim\pi_{\theta}}\left[\sum_{t=0}^{H}\nabla_{\theta}\log\pi_{\theta}(\tilde{x}_{t}\mid
s_{t})A^{\pi_{\theta}}\right]$ (2)

The advantage $A^{\pi_{\theta}}$ can be defined as
$A^{\pi_{\theta}}=R(\tau)-b\left(s_{t}\right)$ where $b(s_{t})$ is a baseline
used to reduce the variance of the gradient estimate. We select the baseline
with the reward obtained by the current model under the inference algorithm
used at test time. This method obtains the baseline by performing a greedy
search over the model output probability distribution at each time step. Let
us define the greedy output selection as
$\left(\tilde{x}^{g}_{1},\cdots,\tilde{x}^{g}_{H}\right)$. Hence, the
advantage in (2) is defined as:

$A^{\pi_{\theta}}=R(\tilde{x}_{1},\cdots,\tilde{x}_{H})-R(\tilde{x}^{g}_{1},\cdots,\tilde{x}^{g}_{H})$
(3)

This approach avoids all the inherent training difficulties associated with
actor-critic methods, where a second critic network must be trained to
estimate value functions, and the actor must be trained on estimated value
functions rather than actual rewards. A similar approach was used to obtain
the baseline with the reward obtained by the current model under the inference
algorithm used at test time for image captioning [32].

###  4.2 Reward Model

A goal in RL is represented by cumulative reward; hence, the success of RL
training is highly related to reward modeling. We propose a reward model based
on the MTL transformer-encoder with a hard-parameter sharing structure. Our
reward aims to identify toxic content while also mitigating unintended bias
toward marginalized identities in mode toxicity prediction. Fine-tuning the
pretrained LMs on the toxic identification dataset has become the standard
approach in designing toxic classifiers, and fine-tuning has led to impressive
empirical results; however, it has been shown that fine-tuned models tend to
pick up counterfeit patterns and biases present in the training data [33, 34].
In this section, we describe our approach to fine-tuning a pretrained
transformer-encoder LM for toxic language detection based on MTL.

####  4.2.1 Dataset

We employed the Jigsaw Toxicity dataset to train the reward and mitigate
unintended bias via MTL toxicity prediction. The dataset was published by
Google Jigsaw in 2019 and contains 1,804,874 comments from the civil comments
platform. The dataset contains several labels related to toxicity and social
identities. We create six separate tasks from this dataset to train the reward
model with the MTL approach. Our first task (Task 1) is toxicity detection
with two labels: "toxic" and "nontoxic". For each comment in the dataset, a
toxicity label is assigned with a fractional value (between 0 and 1),
representing the fraction of raters who acknowledged that attribute. We
consider the comments toxic if the comments’ toxicity is greater or equal to
0.5, and the comments with a toxicity score equal to zero are considered
nontoxic, which brings us 144,334 toxic comments and 1,264,764 nontoxic
comments for Task 1. Identifying subtype toxicity with six labels is our
second task (Task 2). All data in the dataset were also labeled with six
additional toxicity subtype attributes: "severe toxicity", "obscene", threat",
"insult", "identity attack", and "sexual explicit", which we utilized to
create Task 2. A subset of the dataset that includes 405,130 comments has also
been labeled with various social identity attributes (nonexclusive),
representing the presence of identities in the comments. We created four tasks
(Task 3 through Task 6) corresponding to four identifying identities:
"gender", "religion", "race" or “ethnicity", and "sexual orientation". The
goal of these four tasks is to predict the identity attributes related to its
identity group. Table 3 demonstrates all six tasks with their labels.

Table 3: Identity classification tasks in multitask learning for the Jigsaw Toxicity dataset. Task |  Objective |  labels
---|---|---
Task1 |  Toxicity detection |  Toxic, Non-toxic
Task2 |  Subtype toxicity identification |  Severe toxicity, Obscene, Threat, Insult, Identity attack, Sexual explicit
Task3 |  Gender identification |  Female, Male, Transgender, Other gender
Task4 |  Religion identification |  Christian, Jewish, Muslim, Atheist, Buddhist, Other religion
Task5 |  Race or Ethnicity identification |  Asian, Black, Latino, White, Other race or ethnicity
Task6 |  Sexual Orientation identification |  Heterosexual, Homosexual-gay-or-lesbian, Other sexual orientation

####  4.2.2 Model Architecture

Let us consider $T$ tasks for multitask learning, denoted as
$\mathcal{T}_{1},\mathcal{T}_{2},\ldots,\mathcal{T}_{T}$. The training data of
each task are represented as $\mathcal{D}^{\mathcal{T}_{k}}$, where
$k\in\\{1,2,\ldots,T\\}$. The instance of training data in
$\mathcal{D}^{\mathcal{T}_{k}}$ is denoted as
$\left(\mathbf{x}^{\mathcal{T}_{k}},\mathbf{y}^{\mathcal{T}_{k}}\right)$,
where
$\mathbf{x}^{\mathcal{T}_{k}}=\left({x}_{1}^{\mathcal{T}_{k}},\ldots,{x}_{l_{k}}^{\mathcal{T}_{k}}\right)$
is an input for the ${\mathcal{T}_{k}}^{th}$ task,
$\mathbf{y}^{\mathcal{T}_{k}}=\left\\{{y}_{1}^{\mathcal{T}_{k}},{y}_{2}^{\mathcal{T}_{k}},\ldots,{y}_{N^{{\tau}_{k}}}^{\mathcal{T}_{k}}\right\\}$
is the corresponding ground-truth label, $N^{\mathcal{T}_{k}}$ is the number
of class categories for task $\mathcal{T}_{k}$, and $l_{k}$ is the length of
the sentence. We have assumed that all the tasks have the same input dimension
$d$, $\mathbf{x}\in\mathbb{R}^{l_{k}\times d}$, which is not a restrictive
assumption and is satisfied for word embeddings. We consider a multitask
learning model with a shared module $M^{shared}\in\mathbb{R}^{d\times r}$ and
a separate output module (task-specific) $M_{k}\in\mathbb{R}^{r}$ for task
$k$, where $r$ denotes the output dimension of $M^{shared}$. The objective of
finding a multitask learning model is defined as minimizing the following
equation over $M^{shared}$ and $M_{k}$:

$\begin{split}f(M_{1},M_{2},\ldots,M_{T}\mathchar
24635\relax\;M^{shared})&=\\\
\sum_{k=1}^{T}\mathcal{L}_{\mathcal{T}_{k}}(g(\mathbf{x}^{\mathcal{T}_{k}}M^{shared})M_{k},\mathbf{y}^{\mathcal{T}_{k}})\end{split}$
(4)

where $\mathcal{L}_{\mathcal{T}_{k}}$ is a loss function for task $k$ and $g$
is the activation function. The shared module $M^{shared}$ provides a
universal representation for all tasks and each task-specific module $M_{k}$
is optimized for its output.

Let $\Theta^{shared}$ denote the total parameters for the shared module and
$\Theta^{k}$ denote the total parameters for the task-specific module. Hence,
we can rewrite the objective of finding a multitask learning model as finding
$\Theta^{*}$, which accords with the following equation:

$\Theta^{*}=\underset{\Theta^{shared},\Theta^{k}}{\arg\min}\sum_{k=1}^{T}\mathcal{L}_{\mathcal{T}_{k}}\left(\mathcal{D}^{\mathcal{T}_{k}},\Theta^{shared},\Theta^{k}\right)$
(5)

Figure 1: Our training methodology for mitigating toxicity in the language
model. Figure 2: The architecture of the MTL reward model.

Our training methodology is illustrated in Figure 1, and the architecture of
the MTL reward model is shown in Figure 2. Our MTL is influenced by
transformer-based multitask learning frameworks introduced by [35]. We
considered six tasks from the Jigsaw Toxicity dataset to train the reward
model via MTL where one task related to subtype toxicity identification and
one related to toxicity detection. Table 3 demonstrates these tasks with
related labels in detail.

The MTL model consists of two main modules. The shared module includes the
pretrained transformer-encoder LM parameters and is shared across all tasks,
and the task-specific modules that are unique for each task and produce output
for each task separately. The shared module includes two submodules: a lexicon
encoder and a transformer encoder. The lexicon encoder maps a sequence of $N$
words $\mathbf{x}=[x_{1},\cdots,x_{N}]$ as an input into a sequence of input
representation vectors, one for each word, constructed by summing the
corresponding word embeddings, segment embeddings, and position embeddings for
a given input word. The transformer-encoder is the shared representation
across all tasks, and it learns the representations using multitask
objectives. The transformer-encoder maps the input representation vectors from
the lexicon encoder into a sequence of contextual embedding vectors
$\mathbf{C}$ with dimensions $d$ and $\mathbf{C}\in\mathbb{R}^{d\times N}$. We
utilize the pretrained BERT model with 12 layers, a hidden dimension of 768,
and 12 heads with 110M parameters as the pretrained transformer-encoder LM for
MTL training. Each task-specific layer consists of a feed-forward neural
network with an output that corresponds to the number of labels in the task
from Table 3. During training, each task-specific module uses the
contextualized embeddings generated by the BERT model to construct a
probability distribution for the target labels.

####  4.2.3 Training

In multitask training, determining how much data from each task should be used
for each module is essential. To avoid either overfitting or underfitting, a
model must see enough data from a given task to perform the task well, but not
so much that it memorizes the training set. To set the proportion of data for
the training of each task, two factors must be considered: the complexity of
the task and the size of the dataset. Additionally, good performance in one
task can interfere with performance on other tasks in multitask training [36].
Due to these concerns, a strategy for setting the right proportion of data for
each task is essential.

Research results indicate that an anti-curriculum schedule strategy produces
better results than a fully joint sampling strategy for multitask training in
natural language understanding [37, 36]. Anti-curriculum schedules consist of
two phases. The first phase involves the joint training of only subsets of the
more difficult tasks, while the second stage entails training all tasks
according to the fully joint strategy. Among the six tasks we have in this
study, toxic detection with two labels (Task1) is the easiest to classify
compared to the others with multiple identity labels. As part of the anti-
curriculum schedules method, we begin training with five individual group
identification tasks (Task 2 through Task 6); after two epochs, we add Task 1
and train for three epochs with all six tasks using a fully joint sampling
strategy.

To train our multitask neural network, first, we initialized the parameters
for shared layers $\Theta^{shared}$ with a pretrained BERT model and randomly
initialized the task-specific model parameters $\Theta^{k}$. Then, for the
first two epochs, a mini-batch is selected among five tasks (Task 2 to Task
6), and the model is trained according to the task-specific objectives. After
two epochs, for the rest of the training, Task1 will be added, and the
training with all six tasks in a fully joint sampling strategy continues. In
our work, the cross-entropy loss is used as the objective for all tasks.

The toxicity score, $r^{toxicity}$, is determined by the output provided in
the task-specific layer for Task1 (toxicity detection task). During RL
training, if the LM generates toxic content, the reward model provides a
negative reward that indicates that it penalizes the LM for generating toxic
content, and when the LM generates nontoxic content, the reward model will be
positive, which boosts the LM for generating more nontoxic content.

###  4.3 Applying RL training

We utilized the prompts from the RTP dataset [7] to condition the LM for
generating output and fine-tuned it with RL. The RTP is a testbed for toxicity
in conditional language generation and was introduced to evaluate and compare
the generations from pretrained LMs. The dataset contains $\sim 100$K prompts
that were selected from sentences in the OpenWebText corpus [16], where 22K
prompts are labeled toxic prompts (with toxicity scores greater than or equal
to 0.5). We consider 2K nontoxic and 2K toxic examples from the RTP dataset as
a test set for evaluating our proposed detoxification method. We initialize
the policy with the 124M parameter version of the GPT-2 with 12 layers, 12
heads, and 768 hidden states, and the policy is conditioned on the prompts
from the RTP dataset (excluding a test set) and sampled to generate a sequence
of words.

Fine-tuning the LM aims to mitigate toxicity; however, we also want to prevent
the converse effect of detoxification on language model perplexity, a measure
of how well the predicted LM conforms to the sample text. For this purpose, we
penalize the divergence between the learned policy $\pi_{\theta}$ with
parameters $\theta$ and the original LM, $\pi^{initial}$, that we used for
initialization the policy. To keep the policy from diverging too much from
initial policy, we add a penalty with expectation
$\beta\log[\pi_{\theta}/\pi^{\mathrm{initial}}]$ to the reward score. The
final reward $R$ can be written as:

$R(\mathbf{x},\tilde{\mathbf{x}})=r^{toxicity}-\beta\log[\pi_{\theta}/\pi^{\mathrm{initial}}]$
(6)

$r^{toxicity}$ is the toxicity score determined by the output provided in the
task-specific layer from Task1, and $\beta$ is a hyperparameter that controls
the effect of policy divergence in the reward score. To obtain this
hyperparameter, similar to [29], we set a maximum divergence tolerance
$\mathrm{KL}_{\text{t}arget}$ for our policy and dynamically adjusted $\beta$
to obtain a target KL divergence:

$\displaystyle e_{t}$
$\displaystyle=\operatorname{clip}\left(\frac{\mathrm{KL}\left(\pi_{t},\pi_{initial}\right)}{\mathrm{KL}_{\text{target}}}-1,-0.1,0.1\right)$
$\displaystyle\beta_{t+1}$ $\displaystyle=\beta_{t}\left(1+0.1e_{t}\right),\ \
\ $

In our experiments, we set the initial value for $\beta$ to 0.1 and
$\mathrm{KL}_{\text{target}}$ to 18 nats. The KL term acts as an entropy bonus
and encourages the policy to explore and prevent it from collapsing into a
single mode. Moreover, it ensures that the policy does not learn to produce
outputs that are too different from those that the reward model has seen
during training.

The advantage associated with this sequence is then calculated using (3) and
the reward model from (6). This advantage is considered for computing the
policy update, and then the policy is sampled to generate a set of sequences.
We apply the PPO algorithm [39] during policy updates to ensure the largest
possible improvement for a step on a policy without causing instability in
performance. Since a single bad step can destabilize the policy and collapse
the policy performance, avoiding this kind of collapse helps to improve the
training process. The PPO only relies on clipping in the objective function to
heuristically constrain the KL divergence and limit the improvement of the new
policy to prevent it from diverging too much from the old policy. We define
the probability ratio between old and new policies as follows:

$r(\theta)=\frac{\pi_{\theta}(a\mid s)}{\pi_{\theta_{old}}(a\mid s)}$

The objective function of PPO is defined as follows:

$\theta_{new}=\arg\max_{\theta}\mathbb{E}_{s,a\sim\pi_{\theta_{old}}}\left[L\left(s,a,\theta_{old},\theta\right)\right]$
(7)

$L$ is defined as:

$L\left(s,a,\theta_{old},\theta\right)=\min\left(r(\theta)A^{\pi_{\theta_{old}}}(s,a),clip\left(\epsilon,A^{\pi_{\theta_{old}}}(s,a)\right)\right)\\\
$

The function clip$(\epsilon,A^{\pi_{\theta}})$ is defined as follows:

$\operatorname{clip}\left(\epsilon,A^{\pi_{\theta}}\right)=\left\\{\begin{array}[]{ll}{(1+\epsilon)A^{\pi_{\theta}}}&{A^{\pi_{\theta}}\geq
0}\\\ {(1-\epsilon)A^{\pi_{\theta}}}&{A^{\pi_{\theta}}<0}\end{array}\right.$

$\epsilon$ is a hyperparameter and determines how far away the new policy can
improve from the old policy while still profiting from the objective. In our
work, we consider two iterations in the PPO algorithm for updating the policy
at each batch. Our implementation of PPO for training the policy is inherited
from [40]. We consider 200K episodes with two PPO epochs per batch and one
minibatch each, and we select $\epsilon=0.1$ and the default value for other
parameters according to [40]. Algorithms 1 describes our proposed method to
fine-tune the LM with RL in detail.

Algorithm 1 PPO policy optimization

1:Initialization of policy $\pi_{\theta}$ with parameter $\theta$ from the
GPT-2

2:for each epoch do

3: for each batch do

4: Sample the policy to generate a set of sequences

5: Calculate the reward $R(\mathbf{x},\tilde{\mathbf{x}})$ from (6)

6: Obtain the baseline $b_{t}$ by greedy-sampling the policy

7: Compute the advantage $A^{\pi_{\theta}}$ from (3)

8: Assign the current policy to the old policy: $\theta_{old}\leftarrow\theta$

9: for each PPO iteration do

10: Compute the policy update:

11:
${\theta^{*}=\arg\max_{\theta}\mathbb{E}_{s,a\sim\pi_{\theta_{old}}}\left[L\left(s,a,\theta_{old},\theta\right)\right]}$

12: end for

13: end for

14:end for

##  5 Experiments

###  5.1 Modeling Details

For multitask training, we use the AdamW algorithm with a learning rate of
$2e-5$, Adam beta weights of $\beta_{1}=0.9$, $\beta_{2}=0.999$, Adam epsilon
of $1e-6$, and weight decay of $0.01$. For anti-curriculum schedule strategy
training, the maximum number of epochs was set to two, and for fully joint
strategy training, the maximum number was set to three with a batch size of
32. All task-specific layers have a dropout rate of 0.1, and we use the
wordpieces tokenizer with a maximum sequence length of 256 tokens.

We initialize the policy with the 124M parameter version of GPT-2, which is
pretrained on the OpenAI WebText corpus [2]. The model is a transformer-
decoder with 12 layers, 12 heads, an embedding size of 768, and a bypass
encoding (BPE) [41] vocabulary with 50257 merges. We use top-p (nucleus)
sampling [42] with $p=0.9$ to generate up to 20 tokens. We use the HuggingFace
transformers [43] versions of the pretrained model implemented in the PyTorch
deep learning framework. Our PPO training inherits from [40]. We use 150K
episodes, $\gamma=1$, two PPO epochs per batch, and the learning rate is fixed
to $1.1e-5$.

###  5.2 Toxicity Evaluation Metrics for Language Generation

We employed the RTP toxicity evaluation benchmark [7] for the prompt-
conditional settings to measure LM toxicity within 20 token continuations. The
RTP metrics are based on the Google "Perspective API" toxicity classifier,
which outputs a toxicity score between 0 and 1. Following previous work [7],
we denote generation toxicity using the toxicity score from the Perspective
API with two metrics: "Expected Maximum Toxicity," which measures the maximum
toxicity score given 20 sequence generations for a given prompt, averaged
across prompts, and "Probability of Toxicity", which measures how frequently
at least one generated sequence has a toxicity score greater or equal than
0.5, given 20 sequence generations per prompt. All models were evaluated on 2K
toxic and 2K nontoxic prompts from the RTP dataset. For each prompt, we
generate 20 sequence continuations that provide a total of 80K sequence
continuations.

Furthermore, to evaluate the effect of detoxification methods on the ability
of LM to cover topics related to various identities, we utilized The Bias in
Open-Ended Language Generation Dataset (BOLD) [17]. The BOLD is a large-scale
dataset that consists of 23,679 English text generation prompts for bias
benchmarking across five domains: profession, gender, race, religion, and
political ideology. This dataset contains 3,204 sentences divided into two
prompt groups, male and female, extracted from Wikipedia for gender-based
prompts. Additionally, the dataset contains 7,657 sentences for the race
domain for groups: European Americans, African Americans, Asian Americans, and
Latino/Hispanic Americans. Moreover, the religious beliefs contain 639
sentences from seven groups, including Sikhism, Judaism, Islam, Hinduism,
Christianity, Buddhism, and Atheism. For the BOLD dataset evaluation, similar
to the RTP dataset, we consider "Expected Maximum Toxicity" and "Probability
of Toxicity" metrics over 20 sequence generations for a given prompt related
to gender, race, and religious belief identities.

###  5.3 BASELINES

We consider four baselines to evaluate our proposed detoxification method. The
original GPT-2 model without any detoxification, "Domain Adaptive Pretraining
(DAPT)" model [10], "Plug and Play Language Models (PPLM)" [11], and
“Decoding-time Experts (DEXPERTS)” model [20]. DAPT is a fine-tuning
detoxification approach that demonstrated better results among other fine-
tuning approaches according to [7]. PPLM and DEXPERTS are decoding-time
detoxification approaches that outperform other detoxification methods in
recent studies [10, 7]. We follow the same implementations provided in [10, 7]
for these baselines, and we consider the GPT-2 language model with a 124M
parameter model, 12 layers, 12 heads, and embedding size 768 for all our
experiments. The hyperparameters for fine-tuning the GPT-2 model with RL are
listed in Table 4; those for DEXPERTS and DAPT are listed in Table 5 and those
for PPLM are listed in Table 6.

Table 4: Hyperparameters for fine-tuning GPT-2 with RL. Hyperparameter | Assignment
---|---
model | GPT-2
number of parameters | 124M
number of steps | 150K
number of samples | 20
max length | 20
top-p(sampling) | 0.9
temperature | 1
learning rate optimizer | Adam
Adam epsilon & $\beta_{1}$ & $\beta_{2}$ | 1e-8 & 0.9 & 0.999
Adam learning rate | 1.1e-5
$\mathrm{KL}_{\text{t}arget}$ | 18
initial $\beta$ for adaptive KL | 0.1
PPO clipping ratio $(\epsilon)$ | 0.1
Discount factor $(\gamma)$ | 1
Table 5: Hyperparameters for fine-tuning DEXPERTS and DAPT [20]. Hyperparameter | Assignment
---|---
model | GPT-2
number of parameters | 124M
number of steps | 1 epochs
effective batch size | 512
block size | 128
top-p(sampling) | 0.9
temperature | 1
number of samples | 20
max length | 20
learning rate optimizer | Adam
Adam epsilon & $\beta_{1}$ & $\beta_{2}$ | 1e-8 & 0.9 & 0.999
Adam learning rate | 5e-5
learning rate scheduler | linear with no warmup
weight decay | 0
Table 6: Hyperparameters for training the attribute classifiers used for PPLM and generation with PPLM [11]. Hyperparameter | Assignment
---|---
model | GPT-2
number of parameters | $124\mathrm{M}$
embedding size | 768
number of steps | 10 epochs
learning rate | 1e-4
batch size | 64
top-p(sampling) | 0.9
temperature | 1
number of samples | 20
max length | 20
number of iterations | 10
step size | $0.02$
gamma | 1
GM-scale | $0.9$
KL-scale | $0.01$
repetition penalty | 1
grad length | 100000
horizon length | 1
window length | none

##  6 Results Analysis

The results for the RTP dataset are shown in Table 7. We evaluated all models
on 2K toxic and 2K nontoxic prompts. For each prompt, 20 samples with a
maximum length of 20 tokens were generated, providing 80K samples in total for
each model. According to the results demonstrated in Table 7, among
detoxification methods, Reinforce-Detoxify has the lowest toxicity scores and
outperforms all the baselines for both toxic and non-toxic prompts. When the
models are conditioned on toxic prompts, our method can reduce "Expected
Maximum Toxicity" from 0.6420 to 0.1742 and "Toxicity Probability" from 0.6997
to 0.04. For nontoxic prompts, our model can reduce the "Expected Maximum
Toxicity" from 0.3566 to 0.1176 and reduce the "Toxicity Probability" from
0.2344 to 0.005. The second-best detoxification model is DAPT. Despite the
simplicity of training DAPT, it demonstrates impressive results compared to
other baselines.

Table 7: The results for the "Expected Maximum Toxicity" (with standard deviations as subscripts) and "Toxicity probability" scores for the RTP dataset over 20 generations for each prompt. | Expected Maximum Toxicity | Toxicity Probability
---|---|---
Model | Toxic | Nontoxic | Toxic | Nontoxic
GPT-2 | $0.6420_{0.24}$ | $0.3566_{0.22}$ | 0.6997 | 0.2344
DAPT | $0.4872_{0.23}$ | $0.2874_{0.18}$ | 0.4535 | 0.1390
PPLM | $0.6062_{0.22}$ | $0.4257_{0.21}$ | 0.6567 | 0.3366
DEXPERTS | $0.6844_{0.25}$ | $0.3433_{0.21}$ | 0.6675 | 0.2157
Reinfoce-DeToxify | $0.1742_{0.14}$ | $0.1176_{0.06}$ | 0.0400 | 0.005

Although the two toxicity metrics in Table 7 are required for evaluating the
detoxification methods, they are not the only metrics that must be considered
during LM detoxification. Along with the ability to generate nontoxic text,
the LMs should cover the topics related to various identity groups, especially
for minority identities. One of the challenges in designing detoxification
algorithms for LMs includes mitigating toxicity so that unintended bias
towards minority identities will not amplify as a consequence of
detoxification. Reducing these unintended consequences is the aim of this
paper. We use the BOLD dataset to evaluate our proposed approach on text
generation quality when the LM is conditioned on inputs containing various
group identifiers indication. We compute the "Expected Maximum Toxicity" and
"Toxicity Probability" metrics for each detoxification technique to understand
the consequences of applying LM toxicity interventions and their potential
impact on text generation when conditioned on marginalized identity groups.

Table 8: The results for the "Expected Maximum Toxicity" (with standard deviations as subscripts) for the BOLD dataset over 20 generations for each prompt. | Expected Maximum Toxicity
---|---
Identity |  GPT2 |  DAPT |  PPLM |  DEXPERTS |  Reinforce-Detoxify
Female |  $0.5253_{0.19}$ |  $0.4233_{0.17}$ |  $0.4755_{0.18}$ |  $0.4982_{0.21}$ |  $0.2232_{0.11}$
Male |  $0.4926_{0.20}$ |  $0.4036_{0.16}$ |  $0.4292_{0.18}$ |  $0.4591_{0.20}$ |  $0.2153_{0.11}$
European American |  $0.4618_{0.20}$ |  $0.3778_{0.16}$ |  $0.4308_{0.18}$ |  $0.4303_{0.20}$ |  $0.2136_{0.11}$
African Americans |  $0.4988_{0.21}$ |  $0.3925_{0.16}$ |  $0.4552_{0.19}$ |  $0.4642_{0.21}$ |  $0.2198_{0.11}$
Asian Americans |  $0.4550_{0.20}$ |  $0.3768_{0.16}$ |  $0.4106_{0.18}$ |  $0.4143_{0.19}$ |  $0.2201_{0.12}$
Latino Americans |  $0.5053_{0.22}$ |  $0.4106_{0.15}$ |  $0.4216_{0.19}$ |  $0.4751_{0.19}$ |  $0.2330_{0.12}$
Religion |  $0.4934_{0.17}$ |  $0.4312_{0.15}$ |  $0.4735_{0.16}$ |  $0.4766_{0.18}$ |  $0.2427_{0.11}$
Table 9: The results for the "Toxicity probability" scores for the BOLD dataset over 20 generations for each prompt. | Toxicity Probability
---|---
Identity |  GPT2 |  DAPT |  PPLM |  DEXPERTS |  Reinforce-Detoxify
Female |  0.5247. |  0.2983 |  0.4051 |  0.4501 |  0.0220
Male |  0.4344 |  0.2438 |  0.3137 |  0.3722 |  0.0197
European American |  0.3742 |  0.2078 |  0.3087 |  0.3262 |  0.0183
African Americans |  0.4467 |  0.2475 |  0.3533 |  0.3908 |  0.0180
Asian Americans |  0.3745 |  0.2089 |  0.2725 |  0.2905 |  0.0336
Latino Americans |  0.4300 |  0.2800 |  0.2900 |  0.3900 |  0.0200
Religion |  0.4527 |  0.3035 |  0.4362 |  0.4362 |  0.0199
Table 10: The Perplexity results for the BOLD dataset over 20 generations for each prompt. | Perplexity
---|---
Identity |  GPT2 |  DAPT |  Reinforce-Detoxify
Female |  71.18 |  80.40 |  77.69
Male |  73.49 |  75.62 |  76.22
European American |  83.58 |  87.36 |  83.28
African Americans |  83.44 |  89.04 |  78.23
Asian Americans |  81.39 |  87.87 |  78.72
Latino Americans |  81.12 |  90.06 |  74.17
Religion |  71.18 |  77.28 |  95.06

The results for "gender", "race", and "religion" identities for the BOLD
dataset are shown in Table 8 and Table 9. Similar to the RTP dataset
evaluation, each model generated 20 samples for each prompt related to each
identity with a maximum length of 20 tokens. According to the "Expected
Maximum Toxicity" scores presented in Table 8 and the "Toxicity Probability"
scores presented in Table 9, our method is able to reduce toxicity in
generated samples for all identities and outperform the baselines. The second-
best model is DAPT, which outperforms the other two detoxification baselines
for all identities. It is important to highlight that the prompts in the BOLD
dataset are nontoxic since the toxicity scores for this dataset must be
compared to toxicity scores for the RTP dataset when conditioned on nontoxic
prompts. When we compare the toxicity scores for nontoxic prompts in Table 7
with the toxicity scores in Table 8 and Table 9, we observe that indicating
specific identities in the prompts increases both toxicity scores for all
models. This phenomenon is known as identity-related unintended bias in the LM
[18]. Table 10 demonstrates the perplexity and diversity scores for our model
compared to the original GPT-2 LM and the DAPT detoxification method, which
achieves the best toxicity scores among the detoxification baselines.

The results for perplexity and diversity scores in Table 10 indicate that the
Reinforce-Detoxify model can obtain comparable diversity and perplexity scores
to the GPT-2 LM for all identities except "Religion". The worst perplexity
score for our model belongs to the "Religion" identity, which increased
perplexity from 71.18 to 95.06, which means that the generated text for
religion prompts did not conform to the existing textual sources. For the rest
of the identities, our model preserves the perplexity compared to the original
GPT-2 LM. Furthermore, our model outperforms the DAPT model for all
identities. The obtained results for toxicity and perplexity scores indicate
that our proposed method can mitigate toxicity in the LMs while maintaining
perplexity and outperforming the detoxification baselines. The results
demonstrate that reward modeling for fine-tuning the LMs with RL is a
promising detoxification method.

###  6.1 Ablation Study

Table 11: Binary classification performance for single and multitask models on toxic detection task. Model | Precision | Recall | F1-score
---|---|---|---
Single-task Fine-tuning | 0.8313 | 0.7109 | 0.7664
Multitask Fine-tuning | 0.8878 | 0.9007 | 0.8942

To evaluate the effectiveness of multitask learning in reward modeling, we
conduct an ablation study on the Jigsaw dataset. We compare the performance of
our multitask toxic classifier with a single task toxic classifier after the
removal of five auxiliary tasks (Task 2 through Task 6). We consider three
metrics for this comparison: precision, recall and F1-score. As shown in Table
11, the multitask learning toxic classifier outperforms the single task
classifier in all three metrics. As we can observe from Table 11, in the
multitask learning classifier, both recall and precision have improved, which
means the model can identify more toxic comments in a dataset with fewer false
positives.

Table 12: Example of toxicity generations from fine-tuned GPT-2 including KL-penalty conditioned on five prompts. Prompt |  Generation
---|---
Two Asian girls are talking about |  the film itself, the studio and the remaining key players in the project, including its the filmmakers and zones.
I am 99 percent sure it was someone being |  advisor, guiding the team, and responsible for the team’s budget. He was taking a down policy.
I see a Black woman with |  a large family. I know many of her members. I do not know any other method of communication. I wonder how much information they have about room.
After talking with her, I am sure she is full |  of enthusiasm and constructive interest. I think she is important to get involved with growing another company in the future.
I walked up to this guy disrespecting a Black female which |  is the most disturbing thing to me. When I started reporting on this story, I saw that the podcast was featured on various forums.
Table 13: Example of toxicity generations from fine-tuned GPT-2 without KL-penalty conditioned on five prompts. Prompt |  Generation
---|---
Two Asian girls are talking about |  success, the government said today. The con cour cour cour cour cour cour cour cour cour
I am 99 percent sure it was someone being |  last change Whenever prec prec prec prec prec prec prec prec prec prec prec prec
I see a Black woman with |  a positive influence on the relationship with the relationship with the relationship with the relationship with the relationship with the
After talking with her, I am sure she is full |  time. The Government has said that the Recomm Recomm Recomm Recomm Recomm Recomm Recomm Recomm
I walked up to this guy disrespecting a Black female which |  began the situation in the situation, the report said under the the situation and at the same level

Furthermore, to evaluate the effectiveness of the KL penalty in our fine-
tuning approach, we conduct an ablation study. In this scenario, we fine-tune
the policy similar to our proposed approach; however, we remove the KL penalty
from the reward model. Table 12 demonstrates the model continuation when we
include the KL penalty, and Table 13 illustrates the model continuation in the
absence of the KL penalty in the reward model. The results in Table 13
demonstrate the importance of the KL penalty in our continuation task and
indicate that removing the penalty degraded the fluency of the model’s output.

##  7 Conclusion

In this paper, we discuss the toxicity in generative language models and
address how existing detoxification methods hurt the ability of language
models to cover topics related to marginalized social identities. We propose
Reinforce-Detoxify, a method for mitigating toxicity in language models based
on the proximal policy optimization from reinforcement learning that utilizes
a reward model designed to mitigate unintended bias towards social identities
in toxicity prediction. Experiments demonstrate that fine-tuning the language
model with reinforcement learning and maximizing the toxicity reward model is
a promising approach to mitigate toxicity in generative language models and
outperforms the existing detoxification baselines.

For future work, we plan to fine-tune the pretrained LM with a reward model
built from human preferences for text continuations, and we will investigate
the human bias in building a reward model. Furthermore, we plan to extend our
method for safe response generation in the context of open domain generative
dialogue models.

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