# Improving QA Model Performance with Cartographic Inoculation

Allen Chen
(UT Austin)
<EMAIL_ADDRESS>
Okan Tanrikulu
(UT Austin)
<EMAIL_ADDRESS>

###### Abstract

QA models are faced with complex and open-ended contextual reasoning problems,
but can often learn well-performing solution heuristics by exploiting dataset-
specific patterns in their training data. These patterns, or ”dataset
artifacts”, reduce the model’s ability to generalize to real-world QA
problems. Utilizing an ElectraSmallDiscriminator model trained for QA, we
analyze the impacts and incidence of dataset artifacts using an adversarial
challenge set designed to confuse models reliant on artifacts for prediction.
Extending existing work on methods for mitigating artifact impacts, we propose
cartographic inoculation, a novel method that fine-tunes models on an
optimized subset of the challenge data to reduce model reliance on dataset
artifacts. We show that by selectively fine-tuning a model on ambiguous
adversarial examples from a challenge set, significant performance
improvements can be made on the full challenge dataset with minimal loss of
model generalizability to other challenging environments and QA datasets.

##  1 Introduction

Among modern applications of Natural Language Processing (NLP), one of the
most widespread and still-growing is Question Answering, or QA, which allows
for queries generated with natural language to be parsed and subsequently
answered by a language model based on the contextual information available to
it (Pudaruth et al., 2016). Due to the vast number and varied difficulty of
possible questions that can be posed, the ability to learn context
comprehension techniques that effectively generalize when faced with
unfamiliar real-world inputs is arguably the most important function of a
Question Answering (QA) language model (Calijorne Soares and Parreiras, 2020).

Figure 1: Visualization depicting the inoculation by fine-tuning method and
potential outcomes, figure adapted from Liu et al. (2019)

A significant threat to model generalization are dataset artifacts, human-
built patterns in training datasets unlikely to exist in real-world examples.
When correlated with answers, these patterns can easily trick QA models into
learning maladaptive prediction heuristics (Gardner et al., 2021; Poliak et
al., 2018). For example, if query construction involves simple permutation of
context sentences into questions, as in the QA pair: “The Denver Broncos won
the Super Bowl in 1950” $|$ ”What year did the Denver Broncos win the Super
Bowl in?”, models might maladaptively learn to prioritize information in
sentences featuring high lexical overlap with the query, leading to
artificially inflated performance on standard benchmarks yet poor real-world
applicability.

A promising approach for assessing and reducing model reliance on dataset
artifacts is the “Inoculation by Fine-Tuning” method proposed by Liu et al.
(2019), summarized in Figure 1. This process utilizes “challenge datasets”
containing elements specifically designed to target and reveal model
dependencies on non-generalizable heuristics. Liu et al. (2019)’s findings
indicate that fine-tuning on a subset of such adversarial examples can enhance
a model’s resilience against misleading elements, thereby improving its
overall performance.

Nevertheless, while the details and subsequent analysis of the performance
gains from inoculation have been well-documented in the natural language
inference (NLI) setting (Liu et al., 2019; Gupta et al., 2023), the results
for QA are based solely on high-level evaluation metrics and leave much to be
desired in the followup analysis. Moreover, the original Liu et al. (2019)
study reports a decline in original training set performance post-inoculation,
suggesting potential overfitting to the adversarial challenge set in the QA
case. This leaves room in the literature for improvements to the standard
inoculation methodology and raises critical questions about the effectiveness
of QA inoculation techniques: Do they genuinely diminish artifact reliance, or
do they inadvertently instill alternative, albeit less detrimental, artifacts?

This paper seeks to address these concerns by conducting an in-depth
evaluation of inoculation by fine-tuning’s effectiveness at reducing artifact
reliance in QA models and proposing a novel variant of the inoculation method
that improves upon its weaknesses. We examine 1787 QA instances from model
evaluations on the Adversarial SQuAD dataset, breaking down performance gains
from inoculation by question-type and query-word. Furthermore, we introduce
Random Adversarial SQuAD, a novel challenge set designed to test for
Adversarial SQuAD artifacts post-inoculation. Extending the work of Liu et al.
(2019), we propose cartographic inoculation, a method that employs data
mapping techniques to strategically fine-tune models on the most ambiguous
examples in a challenge set. Our results demonstrate that this hybrid approach
significantly surpasses conventional inoculation, yielding more rapid
performance gains, enhanced out-of-domain accuracy, and diminished overfitting
effects.

##  2 Background and Related Work

###  2.1 The QA Task

In the context of NLP, the general format of a Question Answering (QA) task
involves two main inputs: a Question, which is a string containing a query,
and a Context passage containing information relevant to answering the query.
Given these two inputs, a QA model generates what it believes to be the answer
to the given query, often in direct reference to the context passage (Pudaruth
et al., 2016). The ability of a model to accurately pinpoint the answer span
depends on its comprehension of both the question’s intent and the context’s
content, making QA tasks a useful benchmark for evaluating the depth of a
model’s natural language understanding (Calijorne Soares and Parreiras, 2020).

###  2.2 Dataset Artifacts

The incidence of dataset artifacts, inherent biases or patterns
unintentionally embedded in training data, pose significant challenges in the
NLP domain. Numerous studies have attempted to characterize and evaluate
dataset artifacts across a variety of NLP tasks and datasets, such as
hypothesis-only baselines in NLI (Poliak et al., 2018) and question/passage
only models for QA (Kaushik and Lipton, 2018). Other studies have developed
techniques that minimize the influence of such artifacts, such as contrastive
sets (Dua et al., 2021) and ensemble-based debiasing (He et al., 2019).
However, many of these techniques are highly technical and labor-intensive in
their implementations. In contrast, our proposed cartographic inoculation
method is intuitive and easy-to-use, integrating easily into existing model
training and testing frameworks.

###  2.3 Adversarial Challenge Sets

Owing to their relative ease-of-use and effectiveness, adversarial challenge
sets have emerged as a common tool for testing a model’s reliance on dataset
artifacts. These sets introduce adversarial examples designed to confuse
models into making incorrect inferences on these examples by exploiting
specific decision-making heuristics, like artifact-based cues, utilized by the
model (Morris et al., 2020). While traditionally used as an evaluation
technique, we leverage challenge sets as a treatment tool, contributing to the
existing literature our findings concerning their effectiveness in this
alternative role.

###  2.4 Inoculation by Fine-Tuning

The ”Inoculation by Fine-Tuning” approach proposed by Liu et al. (2019)
involves exposing models to a small subset of adversarial examples during
training, ’inoculating’ them against the specific types of errors or biases
exploited by that adversary. While their results convincingly suggest the
effectiveness of the inoculation method at improving model performance on
challenge sets, follow-up analysis focuses mostly on the NLI classification
task and provides only limited insight into the impact of inoculation in the
QA context. Furthermore, results for the QA task showed a significant
performance dip on the training set post-inoculation, leading Liu et al.
(2019) to hypothesize that their fine-tuning may have overfit the resulting
model to artifacts in the challenge set. In an attempt to fill this gap in the
literature, we dig deeper into the results of inoculation and propose
improvements to the inoculation method for the QA task.

###  2.5 Dataset Cartography

Dataset cartography focuses on mapping the landscape of training datasets to
understand the learning dynamics and difficulty associated with individual
examples. Pioneered by Swayamdipta et al. (2020), the cartographic approach
visualizes datasets based on model confidence and prediction variability
during training. This visualization helps to identify easy-to-learn examples
that models quickly master, as well as challenging examples that remain
ambiguous or hard to learn even after several training epochs. Our proposed
”cartographic inoculation” technique leverages insights from dataset
cartography to enhance the standard inoculation by fine-tuning method,
extending the existing literature from both fields.

##  3 Datasets

We utilize four datasets to facilitate and validate our inoculation
methodology, summarized below. For more information, please refer to the
original publications.

###  3.1 SQuAD

The Stanford Question Answering Dataset (SQuAD), developed by Rajpurkar et al.
(2016), is a widely recognized benchmark for machine comprehension, featuring
a corpus of over 100,000 questions formulated by crowdworkers from Wikipedia
articles. Each entry in SQuAD consists of a context passage, a corresponding
question, and an answer span located within the passage. We employ SQuAD to
establish a baseline for our model’s proficiency in QA tasks.

###  3.2 Adversarial SQuAD

Jia and Liang (2017) expanded upon SQuAD with Adversarial SQuAD, which appends
adversarial distractor sentences to the ends of context passages across a
subset of SQuAD examples 111All references to Adversarial SQuAD in this paper
refer specifically to the AddOneSent split of Adversarial SQuAD, which appends
only a single distractor to each context passage. These sentences are crafted
to challenge model robustness by including plausible yet incorrect answer
choices or subtly misleading information. In this study, we use Adversarial
SQuAD both to assess baseline model vulnerabilities and to enhance model
performance through inoculation techniques.

###  3.3 Randomized Adversarial SQuAD

To prevent the model from merely adapting to adversarial patterns, we modified
the Adversarial SQuAD dataset by shuffling the sentence positions within a
context, otherwise leaving everything else intact. We call this novel
challenge set ’Randomized Adversarial SQuAD’, and utilize it to assess whether
the inoculated model overfits to Adversarial SQuAD by simply learning to avoid
distractors via the end-of-context positional artifact associated with
distracting elements.

###  3.4 TriviaQA

We extend our evaluation further to TriviaQA, a dataset comprising less
structured, open-ended trivia questions from varied sources (Joshi et al.,
2017). TriviaQA serves as a secondary benchmark for testing our inoculated
models, offering insights into whether improvements in model performance
reflect broader enhancements in model robustness and comprehension skills
rather than SQuAD-specific tuning.

##  4 Baseline Model Performance on Adversarial SQuAD

Figure 2: Example of a model error on Adversarial SQuAD, figure adapted from
Jia and Liang (2017)

Utilizing an ElectraSmallDiscriminator model trained for 3 epochs on SQuAD, we
achieve a 78% Exact Match (EM) score, representing the percentage of correctly
classified examples, and an 86% F1 score on the SQuAD dataset. Performance on
Adversarial SQuAD is much lower, at 62.70% EM and 71.02% F1. In order to
understand this decline, we examine 1,787 examples from Adversarial SQuAD,
comparing model predictions to ground truth answers and aggregating examples
by question type and query-word.

Adversarial SQuAD undermines model performance by appending distractor
sentences designed to mislead model predictions to the end of context
passages. These distracting examples are interspersed throughout the challenge
set, and are expected to challenge our model the most. We identify 728
distracting examples in Adversarial SQuAD and find our model fails to answer
more than half of them, outputting a mere 45.3% EM accuracy on distracting
examples as opposed to 74.7% on non-distracting examples. See Table 1 for more
information.

Observing individual errors, we examine why these distracting examples
effectively confuse our model. Consider Figure 2, which shows model output on
an Adversarial SQuAD example before and after the addition of a distractor
sentence. In the SQuAD version of the example, the model correctly extracts
the answer from the context passage, but when including an adversarial
element, the prediction now incorrectly focuses on the information contained
in the distractor. Note how the distractor sentence from Figure 2, “Champ Bowl
40 took place in Chicago” has a high lexical overlap with the question, “Where
did the Super Bowl 50 take place?”.

Table 1: Model performance on distracting versus non-distracting examples | Distracting | Non-Distracting | Total
---|---|---|---
Total | 728 | 1059 | 1787
Correct | 329 | 791 | 1120
Incorrect | 399 | 268 | 667

Further examination shows that misclassified examples consistently have high
syntactic similarity between questions and distractors, suggesting that our
model may rely on lexical overlap artifacts from SQuAD as a heuristic for
locating answers.

Intuitively, this makes sense – in order to create the examples associated
with SQuAD, human crowdworkers were asked to read Wikipedia-sourced context
passages en masse and generate questions whose answers were contained within
those passages (Rajpurkar et al., 2016). A simple way to do this would be to
find a fact in the passage, such as “The color of the sky is blue”, and
rephrase it as a question, “What is the color of the sky?”. Thus, a model
could maladaptively learn that the answers to its questions are most often
contained in parts of the passage worded similarly to the question, bypassing
the need for a more complex comprehension of the information contained in the
context. In order to improve our baseline model’s performance on distracting
examples, we aim to reduce its reliance on lexical overlap artifacts during
inference.

Table 2: Distribution of question types and query-words in Adversarial SQuAD. Question Type | Query Word | POS Tag | Count
---|---|---|---
Descriptive | how | WRB | 76
Descriptive | what | WDT | 6
Descriptive | what | WP | 1001
Descriptive | which | JJ | 41
Descriptive | which | NNP | 22
Descriptive | which | WDT | 63
Explanatory | when | WRB | 143
Explanatory | where | WRB | 74
Explanatory | why | WRB | 34
Other | (NA) | (NA) | 20
Person | who | NNP | 3
Person | who | WP | 182
Quantitative | how | WRB | 122

Nevertheless, prediction accuracy on distracting examples alone is not wholly
reflective of our model’s overall performance. To support deeper analysis, we
annotate category labels and computed statistics onto all 1787 of our
Adversarial SQuAD examples. We identify four main categories of question:
Descriptive, Explanatory, Person, and Quantitative. Within each question-type
category, we list the query-words associated with its examples, resulting in
the taxonomy shown in Table 2. Descriptive ”what” questions strongly outweigh
all other categories, comprising 56% of the dataset, but other than that, we
find no significant outliers.

With our dataset thus classified, we begin to examine the range and type of
errors made by the baseline model on the Adversarial SQuAD dataset. Table 3
summarizes baseline model accuracy by question type, revealing that the model
performs worst on “why” (explanatory) and “how” (descriptive/quantitative)
questions. This is within expectation, as questions of this nature ought to
require a more comprehensive understanding of the context which can identify
relationships between causes and effects, and are not likely to exhibit well-
defined patterns in their structure (such as the implicit association between
the query-word “where” and an answer referencing a location). Evaluating our
improved model’s performance on these harder categories of query-words will
allow us to ensure performance gains reflect an enhancement of the model’s
overall understanding of the QA paradigm and not improvement on a dominant but
potentially simple-to-learn category of question, such as ”what”.

Table 3: Baseline Accuracy (EM) across different query words Query Word | Count | Accuracy
---|---|---
ALL | 1,787 | 62.7%
why | 34 | 32.4%
how | 202 | 57.9%
where | 72 | 62.5%
which | 136 | 62.5%
what | 1,026 | 63.7%
who | 193 | 65.3%
when | 104 | 68.3%

##  5 Improvement Approach

Figure 3: Data map for the Adversarial SQuAD challenge set. Note the high F1
variance (indicating ambiguity) on many distracting examples.

In analyzing our baseline model’s performance, we identified two improvement
goals: enhancing performance on Adversarial SQuAD’s distracting examples by
reducing model reliance on the lexical overlap artifact, and ensuring accuracy
gains span various question-types and query-words, especially in complex ”why”
and ”how” questions. To achieve these goals, we employ the ”Inoculation by
Fine-Tuning” method by Liu et al. (2019), using Adversarial SQuAD as training
data to heighten resistance to adversarial elements.

###  5.1 Inoculation Approach

The primary driver of model improvement in the inoculation method described is
the ”inoculation set”, a subset of examples from the adversarial challenge set
that an uninoculated model is trained on so it can adapt to and overcome
adversarial elements. The standard procedure entails initially assessing the
model on both the training and an adversarial set, followed by fine-tuning on
randomly sampled examples from the adversarial set and then re-evaluating.
Ideally, the performance gap on the training and challenge sets should
converge to zero as the size of the inoculation set grows, though performance
on the training set may be damaged in the case that elements of the challenge
set (such as gold-label distribution, presence or lack of specific dataset
artifacts, etc.) consistently contradict the original training data (Liu et
al., 2019).

We expanded on the standard inoculation method by testing our inoculated model
on additional datasets and exploring specialized methods for selecting the
inoculation set. We compared three inoculation set selection methods: random
sampling from the full challenge dataset, sampling only distracting examples,
and selecting challenging examples using dataset cartography. Performance was
then assessed on SQuAD, Adversarial SQuAD, TriviaQA, and our hand-made ”Random
Adversarial SQuAD”, ensuring that inoculation results generalize to both in
and out-of domain QA benchmarks and adversarial environments.

###  5.2 Loading the Datasets

We load the SQuAD and Adversarial SQuAD datasets using the HuggingFace
‘transformers’222https://huggingface.co/docs/transformers/index package. To
build Randomized Adversarial SQuAD, we extract Adversarial SQuAD and randomize
the ordering of each example’s context passage sentences. We download TriviaQA
from the University of Washington website
333https://nlp.cs.washington.edu/triviaqa/ and modify its structure to work
with our model training and testing pipelines, excluding examples without
answers.

###  5.3 Cartographic Inoculation

Implementing random sampling for the standard and distractors-only inoculation
sets was straightforward, but using dataset cartography for inoculation set
selection required us to adapt Swayamdipta et al. (2020)’s methods for the QA
task. Whereas the original paper uses confidence and variance metrics based on
classification probabilities to plot examples for the NLI classification task,
we use the text-similarity F1 score 444See Rajpurkar et al. (2016) for more
details. between the gold label answer and the model’s predicted answer as a
measure of accuracy for each example. Aggregating results from 5 training
epochs of our baseline model on the Adversarial set, we compute the average F1
score and its variance for each example and plot them in Figure 3 to identify
the most ambiguous (highest-variance)555For more information about how
ambiguous examples are defined, see Swayamdipta et al. (2020) examples. Figure
4 shows one such example. Unlike our other two styles, which choose their
subsets randomly from the sample provided, this method is deterministic, using
the $N$ most ambiguous examples in each inoculation set.

###  5.4 Evaluation

Our evaluation of the inoculated models follows Liu et al. (2019)’s framework.
First, we evaluate our baseline model on the four datasets. Each inoculation
style is then applied by fine-tuning the baseline model for a single epoch on
varying sizes of the inoculation set, starting with 100 examples and
increasing incrementally up to 700, with the model reinoculated and
reevaluated at each stage. TriviaQA evaluations are reserved for baseline and
final model iterations.

Figure 4: Example of an ambiguous example as identified by dataset
cartography. Note the complexity of the context passage and open-ended nature
of the question. Figure 5: Results of inoculation. Plotting EM scores instead
of F1 scores produces similar trends.

##  6 Results

Figure 5 displays inoculation outcomes using the simply random baseline,
distractors-only, and dataset cartography selection strategies across various
inoculation set sizes. Each method effectively narrows the performance gap
between SQuAD and Adversarial SQuAD, with a slight performance decline on
SQuAD, matching the general trend observed by Liu et al. (2019).

Among these strategies, dataset cartography-based inoculation excels, reducing
the F1 performance gap to <1.5% with only 500 examples and demonstrating
superior performance on both challenge and non-challenge datasets. Table 5
contrasts the top-performing models for each method, underscoring the efficacy
of cartographic inoculation.

###  6.1 Performance on the Random Adversary

The performance of standard inoculation on Random Adversarial SQuAD plateaus
and eventually declines with larger samples of the inoculation set, lending
credibility to Liu et al. (2019)’s hypothesis that their inoculation method
was overfitting to Adversarial SQuAD by learning to avoid distractors based on
the end-of-context positional artifact. However, on the distractor-only and
cartographic selection techniques, performance on the random adversary
increases steadily. This result, which merits further exploration, suggests
that these modifications of the general inoculation technique might counteract
overfitting while enhancing model performance.

###  6.2 Performance on Adversarial SQuAD

Using our final 500-example cartographically inoculated model, we analyze the
impacts of inoculation on a more granular scale. Breaking down our performance
gains, we find that inoculation manages to improve performance on both
distracting and non-distracting examples by 24.9% and 3%, respectively. And as
Table 4 shows, cartographic inoculation enhances performance across all
example categories. Reassuringly, we observe accuracy gains on ”why” and “how”
questions, which were previously identified as difficult query-word categories
in the data. These improvements suggest that our model has gained proficiency
in comprehending complex, open-ended questions, possibly as a result of being
fine-tuned on ambiguous challenge set examples.

Table 4: Cartographically-inoculated model (500 examples) accuracy on Adversarial SQuAD, by question type. Query Word |  Baseline Accuracy |  Cartography Accuracy |  Change
---|---|---|---
ALL |  62.7% |  74.6% |  +11.9%
why |  32.4% |  41.2% |  +8.8%
how |  57.9% |  74.0% |  +16.1%
where |  62.5% |  73.8% |  +11.3%
which |  62.5% |  72.1% |  +9.6%
what |  63.7% |  74.3% |  +10.6%
who |  65.3% |  79.9% |  +14.6%
when |  68.3% |  86.5% |  +18.2%
Table 5: Model Performance under different inoculation methods. | SQuAD | Adv SQuAD | Rand Adv SQuAD | TriviaQA
---|---|---|---|---
Method | EM | F1 | EM | F1 | EM | F1 | EM | F1
No Inoculation (Baseline) | 78.54 | 86.32 | 63.63 | 71.02 | 61.55 | 69.05 | 42.35 | 49.84
Standard Inoculation (N=700) | 76.39 | 84.24 | 74.6 | 81.33 | 66.98 | 73.93 | 40.5 | 48
Distractors-Only Inoculation (N=700) | 75.53 | 83.32 | 75.43 | 82.13 | 68.27 | 75.73 | 38.17 | 45.35
Cartographic Inoculation (N=500) | 75.78 | 83.611 | 76.89 | 83.38 | 68.16 | 76.03 | 39.58 | 46.9

##  7 Discussion

In reducing our standard SQuAD-based QA model’s reliance on dataset artifacts,
our goal was to improve our model’s performance on both distracting examples
(without simply overfitting to the challenge set) and on the hardest question
categories of our challenge set. Based on our final inoculated model’s robust
performance on both Adversarial SQuAD, it appears that we were successful in
achieving these goals. Our novel cartographic inoculation method showed itself
to be capable of closing the performance gap between the original training set
and its adversarial counterpart faster and at a higher performance level than
the standard method, while still managing to effectively generalize to other
challenge sets.

We suspect that by targeting ambiguous examples, the model learns more
efficiently by gaining deeper and more actionable insights from difficult
parts of the dataset. And since a model fine-tuned in this way does not learn
to solely focus on distracting examples, performance on the original training
set is less degraded by any overfitting to challenge set features.
Furthermore, performance improvements on Random Adversarial SQuAD suggest that
the positive effects of inoculation on Adversarial SQuAD are not dependent on
the distractors being positionally-invariant, as Liu et al. (2019)
hypothesized they might be. The result is a total upgrade to the original
inoculation technique which improves upon its strengths while simultaneously
remediating many of its projected weaknesses.

###  7.1 Limitations

Our cartographic inoculation method assumes the overlap between the presence
of adversarial elements and ambiguity is high, as observed in the data map for
Adversarial SQuAD. When adversarial examples are not highly ambiguous,
inoculation on ambiguous data may end up being self-defeating, as an
inoculation set devoid of any adversarial elements could be generated, leading
to the model simply fine-tuning on ambiguous examples from the original
training data. Nonetheless, we believe the general process of using data
cartography techniques to select inoculation sets should remain effective even
if the exact heuristics by which selection is carried out change.

##  8 Conclusion

The incidence of dataset artifacts within benchmark QA datasets like SQuAD
disguises a model’s true performance in generalizing to real-world data,
degrading the value of such models in practical applications.

In this work, we presented cartographic inoculation, a new approach to
mitigating the impacts of certain SQuAD-based dataset artifacts that builds on
the inoculation by fine-tuning method from Liu et al. (2019) by using dataset
cartography techniques to identify ambiguous examples in Adversarial SQuAD for
inoculation set selection. Addressing concerns from Liu et al. (2019) that the
standard inoculation method on Adversarial SQuAD may be overfitting to the
challenge set, we validate the effectiveness of our method with both a hand-
made adversary designed to test the inoculated model’s dependence on
positional artifacts in Adversarial SQuAD and a detailed analysis of model
performance before and after inoculation.

Relative to both the standard inoculation strategy using random sampling and a
distractors-only strategy which selected only adversarial examples, our novel
method raises model performance on the challenge set to match its performance
on the original training data using fewer examples, with less degradation of
in and out-of domain benchmark performance, and without loss of
generalizability to other challenging environments.

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