# Socratic Reasoning Improves Positive Text Rewriting

Anmol Goel, Nico Daheim, Iryna Gurevych
Ubiquitous Knowledge Processing Lab (UKP Lab)
Department of Computer Science and Hessian Center for AI (hessian.AI)
Technical University of Darmstadt
www.ukp.tu-darmstadt.de

###### Abstract

Reframing a negative into a positive thought is at the crux of several
cognitive approaches to mental health and psychotherapy that could be made
more accessible by large language model-based solutions. Such reframing is
typically non-trivial and requires multiple rationalization steps to uncover
the underlying issue of a negative thought and transform it to be more
positive. However, this rationalization process is currently neglected by both
datasets and models which reframe thoughts in one step. In this work, we
address this gap by augmenting open-source datasets for positive text
rewriting with synthetically-generated Socratic rationales using a novel
framework called SocraticReframe. SocraticReframe uses a sequence of question-
answer pairs to rationalize the thought rewriting process. We show that such
Socratic rationales significantly improve positive text rewriting for
different open-source LLMs according to both automatic and human evaluations
guided by criteria from psychotherapy research.

Socratic Reasoning Improves Positive Text Rewriting

Anmol Goel, Nico Daheim, Iryna Gurevych Ubiquitous Knowledge Processing Lab
(UKP Lab) Department of Computer Science and Hessian Center for AI
(hessian.AI) Technical University of Darmstadt www.ukp.tu-darmstadt.de

##  1 Introduction

Figure 1: We use Socratic rationales consisting of question-answer pairs to
improve positive text rewriting by verbalising the rewriting process. Here, we
illustrate this: given a negative thought, we generate Socratic rationales
grounded in Cognitive Behavioral Therapy to reframe the original thought into
a positive thought.

Negative thoughts can have a profound effect on human judgement and well-being
and often overshadow positive thoughts Vaish et al. (2008), because they can
be emotionally deep-rooted and triggering when brought to surface Beck (1979).
Cognitive Reframing is a highly-validated Cognitive Behavioral Therapy (CBT)
intervention technique that aims to address this by identifying and reframing
negative thoughts into positive ones Clark (2013) and has proven useful in
both clinical therapeutic and self-help settings Williams (2001). For example,
a negative thought “I am divorced. I will probably die soon and nobody will
care for my children.” could be reframed to “I am divorced. I got out of an
unhealthy relationship and can finally seek someone who makes me happier.” to
provide a more positive outlook. Such techniques are not always accessible due
to clinician shortages and a lack of self-help resources, but could be made
more accessible using Large Language Models (LLMs).

Recent works in psychology have shown that the rationalization involved in
breaking down negative thoughts into evidence and specific actions can be
beneficial in overcoming negative feelings and thoughts McManus et al. (2012).
This technique is also used in other therapeutic settings like Motivational
Interviewing Miller and Rollnick (2012). One approach to rationalization is
using guided discovery Overholser (2018). Guided Discovery is a collaborative
method between a therapist and client that navigates important topics in
multiple steps to improve rational thinking and adapt client behaviour. The
Socratic method111The terminology for the Socratic method is not consistent
within the literature Carey and Mullan (2004). In the context of this work, we
use the term Socratic reasoning to refer to question-answer sequences that
follow the Socratic method., which uses questioning strategies for uncovering
a person’s beliefs and motivations, has shown to be a useful
operationalization of this technique. Socratic reasoning typically uses
probing questions to elicit alternative perspectives from the other person.
Table 1 lists different types of Socratic questions that are common in
literature and practice.

Large Language Models (LLMs) hold promise to overcome the lack of clinician
support and self-help resources by automated solutions. Notably, it has been
shown that they can effectively guide text generation towards desired
attributes like non-toxicity Liu et al. (2021); Zheng et al. (2023), style
Reif et al. (2022); Tikhonov et al. (2019), or persona Song et al. (2020).
While this makes them attractive for CBT-aware text rewriting with several
public datasets being proposed recently (Ziems et al., 2022; Maddela et al.,
2023; Sharma et al., 2023), they all fail to provide information on the
rationalization process (see Figure 3), despite rationalization being
pervasively used in CBT. This hinders model development and does not make the
thought process behind reframing transparent.

Still, obtaining this information from humans can be costly. At the same time,
step-by-step reasoning and rationalization with LLMs has progressed
significantly outside of mental health applications. For example, models are
used to generate thought chains to justify their reasoning process for solving
complex tasks like math word problems (Kojima et al., 2022, inter alia). The
scaffolded nature of Socratic questioning in guided discovery is analogous to
such approaches to reasoning with LLMs. Therefore, we make use of rationales
that are synthetically-generated from LLMs to augment CBT-aware text rewriting
datasets with Socratic rationales. We show that this significantly improves
positive text rewriting with LLMs that are finetuned using Socratic
rationales.

#### Contribution

We propose SocraticReframe, a novel framework to improve LLM-based text-
rewriting systems with synthetically-generated rationalization steps. We first
enrich three existing datasets, namely CognitiveReframe Sharma et al. (2023),
PatternReframe Maddela et al. (2023) and PositiveReframe Ziems et al. (2022)
with such rationales. Then, we use them to finetune a series of language
models for positive text rewriting. We find that models trained with Socratic
rationales significantly outperform models trained without them. This is
supported by both extensive automatic and human evaluation. Furthermore, we
conduct a series of ablations which shows that the generated text is on
average much more positive when rationales are used and validate their
efficacy using information-theoretic measures.

#### Organization

The rest of the paper is organized as follows. Section 2 provides the
background on Socratic questioning for cognitive reframing and NLP. We then
introduce our framework, SocraticReframe in Section 3 followed by a discussion
on the experimental setup and evaluation criteria in Section 4. The empirical
results are discussed in Section 5 followed by human evaluations in Section 6.
In Section 7, we conclude our study and discuss potential future directions
based on our work, as well as limitations.

##  2 Background & Related Work

Question Type |  Description |  Exemplars
---|---|---
Clarification |  Questions to go deeper into a thought |  “Why do you say that?”
Probing assumptions |  Questions to make someone think about unquestioned beliefs |  “What could we assume instead?”
Probing reasons and evidence |  Questions digging into the reasoning behind a thought |  “How did you know that…?”
Probing implications |  Questions probing the consequences of a thought |  “What is likely to happen if …?”
Probing alternative viewpoints |  Questions about other, equally valid viewpoints |  “What is another way to look at it?”
Question about the question |  Meta-questions about the question itself |  “Why do you think I asked this?”
Table 1:  The six types of Socratic questions with representative exemplars
from Paul and Elder (2019). We synthetically generate Socratic questions
spanning all types to improve cognitive reframing with (Section 3).

###  2.1 NLP and Mental Health

Research on NLP for mental health has primarily focused on utilizing
linguistic features, as well as neural representations, to identify and
analyze mental health conditions such as depression Rinaldi et al. (2020);
Yates et al. (2017), anxiety Juhng et al. (2023); Wei et al. (2021), dyslexia
Björnsdóttir et al. (2023); Gala and Ziegler (2016), autism Cho et al. (2022);
Goodkind et al. (2018), and schizophrenia Mitchell et al. (2015); Sarioglu
Kayi et al. (2017), among others. Typically, these studies rely on
crowdsourced data or annotated social media posts to address the ethical and
privacy concerns associated with medical data Harrigian et al. (2020);
Moßburger et al. (2020); Turcan and McKeown (2019). Recently, efforts have
been made to utilize datasets that are more representative of the interactions
between a therapist and client in real world settings Pérez-Rosas et al.
(2017); Shapira et al. (2022); Howes et al. (2014); Lee et al. (2019); Cao et
al. (2019); Tanana et al. (2015); Shreevastava and Foltz (2021). Additionally,
recent works have utilized synthetic data to enhance the performance of models
in clinical Kazi and Kahanda (2019); Hiebel et al. (2023); Shim et al. (2021);
Lindsay et al. (2022) and mental health Wu et al. (2023b) settings. Our work
aims to contribute to the growing body of literature leveraging positive
psychology Sheng et al. (2023) and LLMs to improve performance on mental
health tasks.

###  2.2 Socratic Questioning

The Socratic method has found wide use in pedagogy Bautista (2014) and
psychotherapy Braun et al. (2015), because it can improve understanding and
enable alternative perspectives without being explicit or direct. Instead, it
uses questioning strategies to leave room for exploration which is also
helpful for other applications, such as tutoring (Macina et al., 2023b).
Recent works have used the step-by-step nature of Socratic questioning to
improve NLP methods. Ang et al. (2023) collect a large-scale dataset from
Reddit annotated with a question type to train Socratic question generation
models. Wu et al. (2023a) develop a benchmark of Socratic-inspired deductive
reasoning patterns to train state-of-the-art textual entailment and question
answering models. Pagnoni et al. (2023) propose Socratic pretraining to enable
a question-driven approach for summarizing documents. The scaffolding of
Socratic questioning also enables LLMs to solve complex problems by
decomposing them into smaller sub-problems. Qi et al. (2023); Shridhar et al.
(2022, 2023) use Socratic methods to improve LLM’s performance on a variety of
reasoning tasks, including math word problem solving and logical reasoning.
Our work uses the Socratic method as the core principle to guide a language
model while generating a reframed thought.

###  2.3 Reasoning with Large Language Models

Finally, related research has explored the abilities of LLMs to reason about
complex problems. For example, Chain-of-Thought prompting (CoT) Wei et al.
(2022) enables a model to generate intermediate reasoning steps before
generating a final answer. Additionally, rationales, or reasoning paths, have
been shown to improve the performance of LLMs when they are generated by
either larger Shridhar et al. (2023) or even the same LLM Huang et al. (2023).
Chen et al. (2023b) and Madaan et al. (2024) show evidence that LLMs can
refine their own outputs to correct mistakes and generate the correct answer.
Motivated by the prevalence of the Socratic method in psychology and the
reasoning capabilities of LLMs, we leverage the synergy between the two and
aim to improve LLM-based thought reframing by teaching them to verbalize a
Socratic reasoning process.

##  3 SocraticReframe

Figure 2: Detailed illustration of our framework SocraticReframe. First, we
generate Socratic rationales using GPT-4 with a few-shot prompt. Then, we use
the generated Socratic rationale to train models for cognitive reframing.
During inference, the model generates the Socratic rationale before reframing
the negative thought.

Cognitive reframing aims to reframe a negative thought into a positive
thought. We approach this with LLMs that generate a positive thought
conditioned on the negative thought and metadata. However, directly training
an LLM to map positive to negative thought does not make the process behind
the reframing explicit, which is important both for interpretability and model
performance. Our method SocraticReframe overcomes this by training the model
to verbalize a Socratic rationale before generating the reframing. We
introduce SocraticReframe in the following.

Formally, cognitive reframing transforms a negative thought
$\mbox{$\mbox{$\mathbf{t}$}$}\in\mathcal{V}^{\ast}$ into a positive thought
$\mbox{$\mbox{$\mathbf{r}$}$}\in\mathcal{V}^{\ast}$. Both are given as strings
constructed from a (model) vocabulary $\mathcal{V}$. In addition to the
negative thought, metadata $\mbox{$\boldsymbol{\psi}$}\in\mathcal{V}^{\ast}$
is often available which, for example, describes the person experiencing
distress and the situation that caused it. One method for automatic reframing
is using an autoregressive LLM with parameters $\boldsymbol{\theta}$ to model
the distribution

$\begin{split}p_{\text{\mbox{$\mbox{$\boldsymbol{\theta}$}$}}}(\mbox{$\mbox{$\mathbf{r}$}$}\mid\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$})&=\prod_{n=1}^{|\mbox{$\mbox{$\mathbf{r}$}$}|}p_{\text{\mbox{$\mbox{$\boldsymbol{\theta}$}$}}}(r_{n}\mid\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$},\mbox{$\mbox{$\mathbf{r}$}$}_{<n})\end{split}$
(1)

paired with a decoding strategy, such as, sampling or greedy decoding. The
model can be both finetuned using paired data $\smash{\mbox{${\cal
D}$}_{\text{train}}=\\{(\mbox{$\mbox{$\mathbf{t}$}$}_{i},\mbox{$\mbox{$\mathbf{r}$}$}_{i},\mbox{$\boldsymbol{\psi}$}_{i})\\}_{i=1}^{N}}$
or used zero-shot with prompting. While intuitive, this does not allow the
model to explicitly reason about and verbalize a rationale of the reframing.
This is both less interpretable and might lead to overly-simplistic
reframings.

Our proposed method SocraticReframe aims to overcome this by making the model
rationalize the thought process behind a specific reframing by means of a
sequence of Socratic question-answer pairs. This means that we introduce an
additional variable $\mbox{$\mbox{$\mathbf{s}$}$}\in\mathcal{V}^{\ast}$ that
is just a string of this question-answer sequence. The model is then tasked to
first generate $\mathbf{s}$ and only then is allowed to generate $\mathbf{r}$.
This is outlined in Figure 2. Hence, the model now becomes

$\begin{split}&p_{\text{\mbox{$\mbox{$\boldsymbol{\theta}$}$}}}(\mbox{$\mbox{$\mathbf{s}$}$}\circ\mbox{$\mbox{$\mathbf{r}$}$}\mid\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$})=\\\
&\qquad\prod_{n=1}^{|\mbox{$\mbox{$\mathbf{s}$}$}\circ\mbox{$\mbox{$\mathbf{r}$}$}|}p_{\text{\mbox{$\mbox{$\boldsymbol{\theta}$}$}}}\big{(}(s\circ
r)_{n}\mid\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$},(\mbox{$\mbox{$\mathbf{s}$}$}\circ\mbox{$\mbox{$\mathbf{r}$}$})_{<n}\big{)},\end{split}$
(2)

where $\mbox{$\mbox{$\mathbf{s}$}$}\circ\mbox{$\mbox{$\mathbf{r}$}$}$ means
that $\mathbf{s}$ is prepended to $\mathbf{r}$. For models that we finetune,
this means that each training example is also augmented with a Socratic
rationale $\mathbf{s}$ such that $\smash{\mbox{${\cal
D}$}_{\text{train}}=\\{(\mbox{$\mbox{$\mathbf{t}$}$}_{i},\mbox{$\mbox{$\mathbf{r}$}$}_{i},\mbox{$\boldsymbol{\psi}$}_{i},\mbox{$\mbox{$\mathbf{s}$}$}_{i})\\}_{i=1}^{N}}$.
During inference, $\mathbf{s}$ is not known and the model generates it before
generating the positive thought. This forces the model to explicitly reason
about why a specific positive thought is generated to reframe a corresponding
negative thought which we expect to improve both performance and
interpretability. In the following section, we discuss how such Socratic
rationales can reliably be synthetically generated for existing datasets by
leveraging LLMs.

###  3.1 Generating Socratic Rationales

Figure 3: A sample Socratic rationale for an instance from COGREF Sharma et
al. (2023) generated with GPT-4 in a few-shot setting. We use clinical
vignettes from psychology literature as few-shot exemplars. The generated
rationale is useful for the given situation and illustrates the iterative
process involved in cognitive reframing. We also generate the Socratic
question type for each step in the generated rationale.

Our goal is to improve cognitive reframing with Socratic rationales. Such
rationales can be time-consuming to write. Therefore, we use synthetically
generated data which has recently been successfully used in text
classification Li et al. (2023), dialogue generation Bao et al. (2023) and
question-answering Riabi et al. (2021), among others.

In particular, we few-shot prompt GPT-4 (Achiam et al., 2023) to generate
Socratic question-answer pairs based on negative thought $\mathbf{t}$,
positive thought $\mathbf{r}$, metadata $\boldsymbol{\psi}$, and in-context
exemplars of Socratic rationales from clinical literature Padesky (1993) to
ensure high quality and groundedness. The generated Socratic rationale
$\mathbf{s}$ then gives explicit reasoning steps for the particular reframing
and can be used to augment the training data $\smash{\mbox{${\cal
D}$}_{\text{train}}}$. This can be seen as a form of data augmentation and
knowledge distillation into the model that is trained for cognitive reframing.
As we will show in Section 6, the Socratic rationales are rated favourably by
human annotators. An example rationale is shown in Figure 3. Details and
examples are in Appendix A and Appendix D.

In addition to generating rationales, we prompt GPT-4 to classify the specific
type of each generated Socratic question according to the six types described
in Table 1, namely “Clarification”, “Probing Assumptions”, “Probing reasons or
evidence”, “Probing implications”, “Probing alternative viewpoints”, and
“Question”. These can be used for further insights into the reframing process.

##  4 Empirical Analysis

We show the effectiveness of our method on multiple datasets for positive text
rewriting that are outlined in Section 4.1. More details on the exact
experimental set-up, such as the models used, are found in Section 4.2 and
Appendix A and the used metrics are introduced in Section 4.3.

###  4.1 Datasets

Table 2 describes the datasets used in this work which we further outline in
the following.

|  POSREF |  PATREF |  COGREF
---|---|---|---
Train |  6,679 |  5,249 |  400
Test |  835 |  18,635 |  200
$\boldsymbol{\psi}$ |  - |  Persona |  Situation
Table 2: Statistics of the used datasets.

We use three recently-released open-source datasets. First, Positive
Psychology Frames (POSREF) (Ziems et al., 2022) which contains tweets where a
hashtag has indicated stress. Each tweet is mapped to a corresponding reframed
text which is grounded in a set of reframing strategies, for example, Growth
Mindset, Optimism, Self-affirmation. POSREF does not contain any metadata.
Then, we use Pattern Reframe (PATREF) (Maddela et al., 2023), a crowdsourced
dataset of negative thoughts that are conditioned on personas. These personas
describe the person experiencing the respective negative thought. Here,
$\boldsymbol{\psi}$ contains the persona-specific information associated with
each example. For instance, a persona from the dataset includes “My mother was
a teacher. My favorite food is a salad. I enjoy nature. I teach a yoga class.
I am single.” Finally, we use Cognitive Reframe (COGREF) (Sharma et al., 2023)
which is an expert-annotated dataset of situations, thoughts and reframes.
Mental health practitioners were prompted with a situation, which can be used
as metadata, to generate a negative and a reframed thought. For example, one
situation from the dataset is “I participated in a hackathon and I lost”.

|  | Content Preservation ($\uparrow$) | Transfer Strength ($\uparrow$)
---|---|---|---
|  | BLEU | BLEURT |  $\Delta$Pos | Acc. (%)
Dataset | Model | FS | CoT | FT | SoC | FS | CoT | FT | SoC | FS | CoT | FT | SoC | FS | CoT | FT | SoC
| LLaMa | 12.6 | 12.1 | 14.3 | 15.7 | 0.53 | 0.51 | 0.56 | 0.58 | 0.54 | 0.55 | 0.55 | 0.58 | 89.15 | 90.02 | 91.46 | 92.45
POSREF | Mistral | 13.1 | 13.3 | 15.1 | 15.9 | 0.55 | 0.55 | 0.58 | 0.61 | 0.49 | 0.48 | 0.51 | 0.52 | 90.10 | 91.16 | 91.10 | 92.09
| ChatGPT | 15.1 | 15.6 | - | - | 0.57 | 0.59 | - | - | 0.54 | 0.57 | - | - | 92.00 | 92.30 | - | -
| LLaMa | 71.9 | 72.0 | 72.3 | 75.2 | 0.64 | 0.64 | 0.66 | 0.69 | 0.81 | 0.80 | 0.83 | 0.85 | 97.02 | 97.23 | 97.67 | 97.90
PATREF | Mistral | 73.0 | 73.5 | 74.1 | 75.1 | 0.65 | 0.66 | 0.67 | 0.68 | 0.78 | 0.77 | 0.80 | 0.82 | 96.51 | 96.01 | 96.79 | 97.00
| ChatGPT | 73.8 | 73.9 | - | - | 0.63 | 0.65 | - | - | 0.81 | 0.83 | - | - | 97.61 | 97.60 | - | -
| LLaMa | 27.1 | 27.0 | 29.0 | 30.1 | 0.59 | 0.59 | 0.61 | 0.62 | 0.67 | 0.67 | 0.69 | 0.70 | 90.12 | 90.12 | 91.48 | 92.30
COGREF | Mistral | 27.9 | 27.5 | 30.0 | 31.7 | 0.61 | 0.60 | 0.62 | 0.63 | 0.68 | 0.69 | 0.70 | 0.72 | 90.94 | 91.67 | 91.88 | 92.56
| ChatGPT | 30.0 | 31.6 | - | - | 0.63 | 0.63 | - | - | 0.69 | 0.71 | - | - | 91.90 | 92.55 | - | -

Table 3: Automatic evaluation results. FS=Few shot. CoT = Chain of Thought.
FT=Finetune with LoRA. SoC=finetune with Socratic rationale. Items in bold
represent the best performance and items in italics represent the second best
performance. Note that we do not finetune ChatGPT. We observe that using
Socratic rationales for fine-tuning models significantly improves the text
rewriting performance across all datasets.

###  4.2 Experimental Set-up

We use different LLMs that we either prompt or finetune. Namely, we prompt
ChatGPT, LLaMa-2 7B Touvron et al. (2023) and Mistral 7B Jiang et al. (2023)
and finetune LLaMa 7B and Mistral 7B for cognitive reframing. All models are
finetuned using LoRA Hu et al. (2021) due to computational constraints. For
generating Socratic rationales, we use GPT-4. We use the transformers Wolf et
al. (2020) library for all our experiments.

We test the models on different setups. The first two use prompting and the
last two use finetuning:

  * •

Few Shot (FS): Few shot exemplars of negative and positive thought are given
to the model which are handcrafted from literature on cognitive reframing.

  * •

Chain of Thought (CoT): The model is asked to think step-by-step and
rationalize the reframed thought. 222We observed similar performance for zero-
shot and few-shot CoT and therefore only report zero-shot CoT. We believe the
similar performance in both setups stems from the nature of the reframing task
where there is extra contextual information in the form of $\boldsymbol{\psi}$
and the LLMs are able to generalize well even without few-shot exemplars.

  * •

Finetune (FT): The model is finetuned using LoRA to reframe a negative thought
without a Socratic rationale.

  * •

Finetune with Socratic Rationales (SoC): The model is finetuned using LoRA to
reframe a negative thought by first rationalising it using Socratic
questioning and then generating the positive reframing. The prompt template
used for finetuning is shown in Figure 8.

###  4.3 Evaluating Cognitive Reframers

We aim to reframe a negative into a positive thought while preserving the
original meaning and semantics. Hence, following Hu et al. (2022); Qu et al.
(2023), we evaluate them along the criteria of Transfer Strength, which
describes how well the thought was transferred to a positive one, and Content
Preservation, which evaluates how well the original meaning of the thought was
preserved. We describe how we measure both of them in the following.

Transfer strength defines how well negative sentiment is turned positive. To
measure it, we first use a finetuned RoBERTa model to evaluate the sentiment
scores of the original and reframed thoughts. Then, we use the pairwise
difference of sentiment scores between the original and reframed thoughts and
report the average, denoted by ($\Delta$Pos). We report the number of samples
with an increase in positivity with respect to the original thought as
accuracy. A decrease in the score from the original thought is considered a
failed case (Acc).

Our aim is to reframe a thought but also preserve the original meaning and not
drastically change it. We rely on the following automatic metrics which have
been widely used in the text reframing literature: BLEU Papineni et al. (2002)
and BLEURT Sellam et al. (2020). Following all three datasets we consider
Ziems et al. (2022); Maddela et al. (2023); Sharma et al. (2023), we report
the BLEU scores for each pair of original and reframed thoughts. Since it has
been shown that BLEU scores do not always correlate well with human judgements
on semantic similarity, we also use a BERT-based BLEU variation, BLEURT-20
which is trained on synthetic samples to get accurate semantic similarity
scores. We report the average over a dataset. Each score is a value between 0
and 1, ranging from no to complete semantic similarity.

###  4.4 Evaluating Socratic rationales

For Socratic rationales to be useful for a model, they should intuitively
contain new salient information that is not contained in the negative thought.
We use the recently-proposed information-theoretic metric REV Chen et al.
(2023a) to measure this. REV uses conditional $\mathcal{V}$-information to
compute the usable information that can be extracted from a variable
($\mathbf{s}$) by a model to predict another variable ($\mathbf{r}$),
conditioned on a third variable
($\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$}$). More formally, we
compute

$\begin{split}\textsc{REV}(\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\mbox{$\mathbf{r}$}$},\mbox{$\boldsymbol{\psi}$},\mbox{$\mbox{$\mathbf{s}$}$})&=-\log\smash{p_{\text{\mbox{$\mbox{$\boldsymbol{\theta}$}$}}^{\prime}}}(\mbox{$\mbox{$\mathbf{r}$}$}\mid\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$})\\\
&\qquad+\log
p_{\text{\mbox{$\mbox{$\boldsymbol{\theta}$}$}}}(\mbox{$\mbox{$\mathbf{r}$}$}\mid\mbox{$\mbox{$\mathbf{t}$}$},\mbox{$\boldsymbol{\psi}$},\mbox{$\mbox{$\mathbf{s}$}$}),\end{split}$
(3)

where $\mbox{$\mbox{$\boldsymbol{\theta}$}$}^{\prime}$ and
$\boldsymbol{\theta}$ are the parameters of two models trained to minimize
cross-entropy, respectively. The REV metric can then be computed for an entire
corpus ${\cal D}$ by averaging the pointwise (per-example) scores. While Chen
et al. (2023a) use fine-tuned models on the specific datasets for computing
the score, Lu et al. (2023) show that it can even be calculated directly from
pretrained LLMs with the same effectiveness as using fine-tuned models Lu et
al. (2023).

Figure 4: The Socratic rationales generated for all three datasets have
positive REV scores, meaning that they indeed provide useful information for
text rewriting. Mean of the example-wise REV values are plotted for all three
datasets.

##  5 Results

In this section, we detail our results obtained with different strategies.
First, our main results in Section 5.1 show that the Socratic rationales
improve text rewriting. Then, we show that the rationales are indeed
informative for the model in Section 4.4.

###  5.1 Main Results

Table 3 shows that using Socratic rationales consistently outperforms both
vanilla finetuning and prompting strategies on all datasets. We show an
example in Table 4 that highlights how the positive rewritings appear much
more thoughtful, because the Socratic rationale forces the model to explicitly
reason about the rewriting process. It is noteworthy that Socratic reasoning
improves both the content preservation as well as transfer strength
capabilities of smaller models beyond the performance of ChatGPT, despite the
model being multiple times larger than LLaMa 7B and Mistral 7B.

Across all datasets, we observe that Mistral performs better in content
preservation while LLaMA performs better in the sentiment of the reframed
thought. This could be due to the different pretraining datasets used for
training the models. We observe that using Socratic reasoning improves BLEU
scores by almost 2 points on average when compared to finetuning without
Socratic rationales. Distilling Socratic reasoning into smaller models helps
to preserve the original meaning and improve the positive sentiment transfer.
This is desirable for users and practitioners alike, because more positive
sentiment will likely benefit users more and content preservation ensures that
the reframing stays relevant for them.

###  5.2 Socratic Rationales are Informative

Similar to previous work for evaluating free-text rationales Chen et al.
(2023a), we use the pretrained GPT-Neo 2.7B Gao et al. (2020) for computing
REV. We report the average REV metric over each sample for all datasets. A REV
value greater than zero implies that the Socratic rationale provides new,
additional information for supporting the reframed thought. In contrast, a
value less than zero implies the rationale does not support the reframe. As
reported in Figure 4, we observe that all three datasets have REV values
greater than zero, signifying the usefulness of enriching the cognitive
reframing task with Socratic rationales.

Table 5 shows the generated rationales for each of the dataset we consider. In
particular, POSREF has the highest informativeness for the reframed thoughts.
We hypothesize two main reasons for this. First, POSREF does not contain any
extra information (situations or personas) like the other two datasets where
the information gets spread out through a large number of tokens. Second,
samples in POSREF are sourced from Reddit and more representative of the
training data of GPT-Neo than the crowdsourced data in the other two datasets.

Method |  Reframed Thought
---|---
Mistral-FS |  I will improve.
Mistral-CoT |  I will improve and learn to be better.
Mistral-FT |  I will learn from the feedback and grow.
Mistral-SoC |  It is normal to feel disappointed. I can use this experience to learn and grow.

Table 4: Sample reframed thoughts generated by Mistral variants for the
original thought: “I submitted a paper to ACL and it got rejected. I will
never succeed as a researcher.” The additional socratic rationale leads to a
more detailed and well-founded reframing.

##  6 Human Evaluation

###  6.1 Evaluating Reframed Thoughts

While previous works in cognitive reframing Maddela et al. (2023); Sharma et
al. (2023) rate the reframed thoughts on a Likert-scale with dimensions like
fluency, helpfulness and readability, we instead consider that the main goal
of such reframing systems is to help users overcome unwanted thoughts.
Therefore, we conduct pairwise comparisons between 100 randomly sampled
reframes generated by our proposed SocraticReframe finetuning strategy vs
either ChatGPT or a baseline model (that does not use the Socratic
rationales). Two computer science graduate students conducted the task. We
report win rate as the number of times a reframe generated by the Socratic
model is preferred by an annotator over the baseline reframe.

Figure 5: Human evaluation results for Mistral-SoC compared to Mistral-FT and
ChatGPT. With a win rate of over 50% human annotators prefer reframes
generated by our method to ChatGPT and simple fine-tuning.

As shown in Figure 5, Mistral-SoC outperforms Mistral-FT by a significant
margin with a win rate of over 65% and tie rate of around 30%. We fit a
Bradley-Terry Model Bradley and Terry (1952) on the preference data and obtain
a strength of 1.3 for Mistral-SoC as compared to strengths -1.8 and 0.5 for
Mistral-FT and ChatGPT, respectively. Intuitively, this means that Mistral-SoC
will have a win rate of 95% over Mistral-FT and 67% over ChatGPT, confirming
the effectiveness of our method.

###  6.2 Evaluating Socratic Rationales

To evaluate the synthetically generated rationales, we consider two key
attributes: whether they represent the use of Socratic questioning in a
clinically meaningful way and the general helpfulness of the questioning to
overcome negative thoughts.

Following Braun et al. (2015), we use the Socratic Questioning Scale (SQS) to
evaluate the quality of generated rationales. SQS contains five questions
rated on a Likert scale, which evaluate the degree to which the Socratic
method was used in a given snippet. The final score is reported as the sum of
the Likert ratings of the five questions. Since SQS was originally developed
for assessing therapy session transcripts, we only include the three questions
which are relevant for evaluating the use of the Socratic Method in a given
text. The three questions evaluate the following dimensions for the rationale:
Open-endedness, Context and Relevance to the situation. The full questions are
reported in Appendix C.1. Additionally, we consider _helpfulness_ as a metric
to assess whether or not the generated rationale is generally helpful in
overcoming negative thoughts. We ask the annotators to rate the generated
rationales on a 3-point Likert scale.

Figure 6: Human evaluation results for the Socratic rationales show that
annotators judge the generated rationales as highly helpful and relevant for
reframing negative thoughts.

Three computer science graduate students annotated 100 randomly sampled
Socratic rationales across the three datasets. Figure 6 shows that the
rationales were rated favorably across all criteria. We obtain an Intra Class
Correlation (ICC) Shrout and Fleiss (1979) of 0.806 for helpfulness and 0.601
for the SQS scale. SQS appears to be particularly challenging to evaluate by
the raters and judgements on the use of the Socratic Method may be subjective.
Our observations align with works in psychology that report similar agreement
scores for the SQS scale Braun et al. (2015). Additionally, we observe a
Pearson correlation coefficient of 0.72 ($p<0.05$) between the helpfulness and
SQS ratings, indicating that when the human raters evaluate a rationale as
containing high-quality Socratic questioning, they also tend to deem it more
helpful for dealing with the negative thought.

##  7 Conclusion & Future Work

In this work, we show that text rewriting models for cognitive reframing can
be improved by using Socratic rationales to verbalize the reframing process.
Because such rationales can be costly to obtain from human annotators, we use
synthetically generated rationales to augment three open-source datasets for
text rewriting. We then train models to verbalize the rationale before
generating the reframing. We show that this leads to substantial improvements
to finetuned open-sources LLMs like LLaMa-2 7B and Mistral 7B when compared to
training without rationales and strong prompt-based baselines. We confirm our
results with an extensive human evaluation that shows high ratings for the
synthetically generated rationales and a clear preference for reframings
generated with our method. By releasing our code and data we hope to enable
future research, for example, on incorporating the different question types
that are annotated, or using the data in therapist training.

## Limitations

While synthetic data generated by LLMs like GPT-4 offers a promising solution
to data scarcity and privacy concerns, a drawback could be a potential lack of
diversity and complexity. Our generated Socratic rationales may not fully
capture all intricate nuances or patterns in authentic therapist dialogues
while performing cognitive change with the Socratic method. A significant
limitation when working with mental health and psychotherapy-based datasets is
the use of heavily curated or crowdsourced data. This can lead to an imbalance
in the demographics and language of our datasets. Since the datasets we use
are sourced from social media or crowdsourcing platforms, the quality of the
negative thoughts might also be limited.

In addition to the reframed thoughts, the datasets considered in this work
also contain the cognitive distortions associated with each negative thought.
Cognitive distortions are thought patterns that lead to negative feelings. We
believe information like cognitive distortions and reframing strategies can be
utilized to further improve the quality of the Socratic rationales and
subsequently enhance the text reframing performance.

Finally, our focus in this work was to improve text reframing which is an
effective short-term in-the-moment strategy. However, we emphasize that
assessing long-term outcomes is imperative and is a future research direction.

## Ethical Considerations

While this work focused on generating Socratic rationales to improve positive
text rewriting, open-ended LLMs still carry the risk of generating harmful
outputs. Therefore, we advise careful consideration before they are applied in
practice. Unsupervised use of the Socratic questioning data as is, without
consulting trained professionals, could be harmful and requires careful design
and implementation before being applied in real-world settings.

## Acknowledgements

This work was supported by the DYNAMIC center, which is funded by the LOEWE
program of the Hessian Ministry of Science and Arts (Grant Number LOEWE 1 16
519 03 09 001 0009 98). This research work has also been funded by the German
Federal Ministry of Education and Research and the Hessian Ministry of Higher
Education, Research, Science and the Arts within their joint support of the
National Research Center for Applied Cybersecurity ATHENE. We gratefully
acknowledge the support of Microsoft with a grant for access to OpenAI GPT
models via the Azure cloud (Accelerate Foundation Model Academic Research).

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##  Appendix A Experimental details

We few-shot prompt GPT-4 with the default parameters from the OpenAI Python
package. 333https://github.com/openai/openai-python We use three few-shot
exemplars. Following similar works on synthetic data generation with LLMs
Macina et al. (2023a), we use temperature sampling with $T=0.4$ and no top-k
truncation for generating the reframes. For the fine-tuning experiments,
following Dettmers et al. (2024), we apply LoRA to all linear layers; training
for 5 epochs with AdamW, a batch size of 8 and set the learning rate to 5e-4.
All other hyperparameters take the default value in the configuration. We use
NVIDIA A100 80GB for the training.

##  Appendix B Prompt Design

###  B.1 Generating Socratic Rationales

System Prompt for GPT-4 You are an expert in Cognitive Behavioral Therapy and
Cognitive Restructuring, focusing on guided discovery. The term cognitive
restructuring refers to the process of challenging, and changing, irrational
thoughts. Socratic questioning is one technique to encourage this process.
Therapists use Socratic questioning verbally by asking probing questions about
their clients’ irrational thoughts. As clients improve their awareness of
irrational thoughts, they can begin to consciously question their own
thoughts. The six types of Socratic Questions are: 1) Clarification, 2)
Probing assumptions, 3) Probing reasons and evidence, 4) Questioning
perspectives, 5) Probing implications and 6) Questioning the question. They
can be used in any order and not all of them might be needed for a given
client.

Figure 7: This system prompt is used to prime GPT-4 with a personality
grounded in the Socratic method.

An example of a clinical vignette taken from Padesky (1993) used as a few-shot
exemplar is:

⬇

Negative Thought: "I’m a complete failure in every way."

Positive Thought: "I am not a failure."

Socratic Questioning:

Q (Clarification): You look defeated when you say that. Do you feel defeated?

A: Yes. I’m no good.

Q (Probing assumptions): You say you are no good. Is it true that you haven’t
done anything at all good?

A: Nothing of importance.

Q (Probing reasons and evidence): How about for your children this week \--
did you care for them at all?

A: Of course, I helped my wife put them to bed and took them to soccer
practice.

Q (Questioning perspectives): Do you think that was important to them?

A: I suppose so.

Q (Probing implications): And did you do anything to make your wife happy this
week?

A: She liked the fact that I came home from work on time.

Q (Probing implications): Would a "complete failure" be able to respond to his
wife’s request in such a successful way?

A: I guess not.

Q (Probing implications): So is it really accurate to say you are a complete
failure in every way?

A: I suppose not.

Q (Clarification): So how do you feel now?

A: I guess a little better.

The prompt template for GPT-4 is shown in Figure 3 and the system prompt is
shown in Figure 7.

###  B.2 Finetuning

Prompt template used during finetuning Input: Given a situation: <<SITUATION>>
and the associated negative thought: <<NEGATIVE THOUGHT>>, generate the
Socratic rationale for guided discovery and reframing the negative thought to
a positive thought. Output: <<SOCRATIC RATIONALE>> <<POSITIVE THOUGHT>>

Figure 8: This template is used to finetune LLMs using the Socratic
rationales.

The prompt template used for finetuning is shown in Figure 8.

##  Appendix C Human Evaluation Protocol

###  C.1 Evaluating Socratic Rationales

#### Socratic Questioning Scale (SQS)

The following questions were rated by annotators on a 3-point Likert scale
ranging from 1 (not at all) to 3 (extensively) :

  * •

How frequently were questions asked that help develop alternative
perspectives? \- This question aims to assess the frequency of inquiries that
encourage the exploration of diverse viewpoints or opinions. Annotators should
focus on identifying questions that prompt respondents to consider different
angles, challenge assumptions, or think beyond the conventional narrative.

  * •

Was the question answering focused on the emotions and situation of the
person? \- This question assesses whether the majority of answers provided
focus on the emotional state and circumstances of the individual involved.
Annotators should consider whether responses primarily address the feelings,
experiences, or immediate context of the person in question.

  * •

Were the questions open-ended and require thoughtful reflection? \- This
question is designed to evaluate whether the questions posed to respondents
are open-ended and demand deep contemplation rather than eliciting simple,
direct answers. Annotators should look for questions that encourage
respondents to think critically and provide nuanced, reflective responses.

#### Helpfulness

Annotators rate the helpfulness of the question-answer pairs on a 3-point
Likert scale ranging from 1 (not helpful at all) to 3 (very helpful). The
following question was asked to the annotators:

##### How helpful was the questioning in general?

This question seeks to gauge the overall effectiveness and utility of the
questions posed. Annotators should consider the extent to which the questions
contributed to a meaningful discussion, elicited insightful responses, or
facilitated a deeper exploration of the topic. Assessments should encompass
the clarity, relevance, and engagement level of the questions.

We plan to release the generated rationales to complement the original
datasets under the Apache
license.444https://www.apache.org/licenses/LICENSE-2.0.

###  C.2 Evaluating Reframes

For a given context and negative thought, annotators are shown two sample
reframes and asked the following question:

  * Given the context and original negative thought, which reframed thought do you prefer (A vs B)?

##  Appendix D Qualitative Examples

Table 5 shows qualitative examples of generated socratic rationales for each
of the three datasets used in our study.

Dataset |  Input |  Generated Rationale
---|---|---
COGREF |  Situation: Having problems coming up with words to write essay. Negative Thought: I will get a bad grade. Reframed Thought: I’m feeling anxious about this assignment. I can use skills to calm myself, try my best on the essay, ask for any appropriate help, and whatever grade I get I will know that I tried my hardest. |  Q: You’re worried about doing badly on this assignment. How have you done on past essays? A: I’ve gotten good grades, sometimes. But this one feels different. Q: Do you think getting a bad grade on this specific essay means you’re generally bad at writing or at this subject overall? A: Well, I don’t think so, but I’m unsure about this topic. Q: What evidence is leading you to believe that you will get a bad grade? A: I’m having trouble coming up with points to write about. Q: If a friend was in your position, what advice would you give them? A: I would probably say to not panic and just give it their best shot. Q: What would happen if you got a bad grade on this essay? How would that affect your overall academic achievement? A: One bad grade isn’t going to ruin my overall performance I guess. Q: Can using some support like talking to your teacher or a study group help you in getting through this? A: That might be a good idea. Q: So given these factors, do you still think it’s a definite that you will get a bad grade? A: No, I suppose it’s not definite. Q: So, what will you do about your essay now? A: I think I’ll start by focusing on writing a draft based on what I know and then ask for some help to see how I can improve it.
PATREF |  Persona: my favorite band is rise against. my favorite team is the raptors. i usually go to bed late. i like sports. i am studying to be a dentist. Negative Thought: I am about to graduate and become a Dentist, but that means nothing. Anyone can do that. Reframed Thought: I am about to graduate and become a Dentist. Although it is possible for anyone to become a dentist, it is very difficult, and I am proud of myself. |  Q: It sounds like you’re doing something remarkable, yet you downplay its value. Do you genuinely believe becoming a dentist means nothing? A: It doesn’t seem special when everyone else is doing similar things. Q: Do you think everyone could become a dentist? A: Maybe not everyone, but a lot of people could. Q: It required you to put in several years of hard study and practice, didn’t it? A: Yes, I did work hard. Q: How many people do you think are willing or able to do what you did? A: I’m not sure, probably not that many. Q: So wouldn’t that mean that your achievement is indeed quite significant and not something anyone could do? A: I guess so. Q: Isn’t it possible then that your achievement of becoming a dentist is both valuable and hard-earned? A: Yes, it’s starting to look that way. Q: How do you feel about your upcoming graduation now? A: I’m starting to recognize its importance and feel a bit proud.
POSREF |  Negative Thought: This physics homework is the death of me every week. It’s so hard. And then I have another physics exam tomorrow night. Reframed Thought: Physics is challenging, but is worth it. I have an exam tomorrow, which I hope to do well on. |  Q: It sounds quite challenging, is Physics always this difficult for you or only certain aspects? A: Certain parts, like quantum mechanics. Q: So, is it accurate to state that the entire subject is difficult, or just the quantum mechanics part? A: I suppose just the quantum mechanics part. Q: Can you give me an example of a physics problem or concept you’ve been able to understand quite well? A: Yeah, I’ve been doing well in classical mechanics. Q: Do you think that possessing skills in one area of physics could potentially aid in understanding other areas, including quantum mechanics? A: Maybe. But quantum mechanics seems very different. Q: If classical mechanics and quantum mechanics are two aspects of the same discipline, isn’t there a possibility that understanding one could help with the other? A: It’s a possibility, I guess. Q: Do you think saying that the physics homework is the death of you is a fair and accurate statement? A: Probably not, it’s just really frustrating sometimes. Q: What if you thought about the physics homework as a challenge rather than a death sentence? Could that change how you approach it? A: Maybe. I could try. Q: Now, how do you feel about the physics exam tomorrow? A: I’m nervous, but maybe if I look at it as a challenge, it might feel less overwhelming.
Table 5: Socratic rationales generated with GPT-4 for different types of
inputs from the three datasets we consider.