Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided

Thank you!

Technical contribution:

As Reviewers WuPB & Zhu4 noted, we propose an innovative framework that induces advanced cognitive capabilities in LLMs, proving effective even at the 7B scale. Besides, as Reviewer LP97 highlighted, we introduce a novel benchmark for evaluating LLMs’ ability to generate cognitive reappraisals.

Narrow focus of this paper:

Reappraisal is a specific and well-defined strategy for providing emotional support, backed by extensive psychological literature with broad applications. There is award-winning literature in NLP on providing reappraisals and “positive reframes” (Ziems et al., 2022; Sharma et al., 2020), so we respectfully disagree with the claim that this topic is “narrow”.

Moreover, RESORT can guide response generation for different theoretical constructs with expert-crafted constitutions. We also demonstrate how to use experts (in our case psychologists) to validate the output. Consistent with prior work (Ayers et al., 2023; Yin, Jia, & Wakslak, PNAS 2024; Li, Herderich, & Goldenberg, 2024), our study underscores the potential of LLMs on providing emotional support.

How are LLM responses better than human oracle ones?

The oracle responses were often perceived as informal, vague, and lacking detailed suggestions or support for the OP. Providing reappraisals is inherently cognitively-demanding, even for experienced psychologists. We hope AI can help “augment human ability” in such tasks, and this work is a first step in that direction.

Case study on with/without appr and cons:

We will discuss Table 6 (Appendix D) in the main body of the paper.

Subjective eval:

We agree that the eval is subjective by nature, hence we rely on expert psychologists as evaluators. Despite the subjectivity, they still reach moderate agreement as shown in Table 2 (Artstein & Poesio, 2008). Evaluators received clear instructions (Figures 7&8) to guide their assessment. For example, the “unguided response” in Figure 2 is unempathetic and lacks specific coping mechanisms, whereas the “guided response” identifies the root cause of distress and provides specific solutions (we will include the full response for clarity).

First example in Table 9:

The suggestions in the response are mostly generic. For example, advice like “It might be helpful … as a whole” is templated and can be applied to any context. In our evaluation, we discourage responses that are not tailored to what the narrator is facing.

Thank you for your positive comments on the innovation of the RESORT framework to induce advanced cognitive capabilities from LLMs, which Reviewers WuPB and LP97 also pointed out. We are also grateful for your appreciation of our rigorous and in-depth human evaluation carried out by expert psychologists, which was recognized as a key strength by Reviewers WuPB and ArmX.

Oracle responses were written by one person and may have subjective biases:

We agree that having one human writer for oracle responses is a limitation, and this is because of our specific methodology (getting an expert to write a response given the same instructions as the LLM, rather than just taking existing Reddit responses like Ayers et al., 2023), which is more costly (around 15 minutes of expert time per post). But despite this limitation, our results are surprisingly consistent with a lot of work (Ayers et al., 2023; Yin, Jia, and Wakslak, PNAS 2024; Li, Herderich, and Goldenberg, 2024), so we don't think that our results with one human writer should be dismissed out of hand. We'll discuss this issue more in our revision.

Performance of LLMs under different constitutions:

We leave the investigation of LLMs under different prompt words to future work.

Other data sources:

To our knowledge, there is no open-sourced data on cognitive reappraisals that is readily available for access, as such data would be subject to user privacy agreements. For the same reason, we would perform anonymization methods when we release the dataset in the camera-ready version of the paper. As Reviewer LP97 noted as a “key strength” of the paper, the release of our carefully curated dataset would greatly benefit the research community.

References

• Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., & Smith, D. M. (2023). Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA internal medicine, 183(6), 589–596.
• Yin, Y., Jia, N., & Wakslak, C. J. (2024). AI can help people feel heard, but an AI label diminishes this impact. Proceedings of the National Academy of Sciences, 121(14), e2319112121.
• Li, J. Z., Herderich, A., & Goldenberg, A. (2024). Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios.

We are grateful for your valuable feedback! We appreciate your recognition of the strength of the RESORT framework in inducing cognitive abilities from LLMs, which Reviewers WuPB and Zhu4 also pointed out. We are also thankful for your appreciation of the systematic and rigorous human evaluation conducted with psychologists holding M.S./Ph.D. degrees, which makes the findings more sound as acknowledged by Reviewers WuPB and ArmX. We address your comments here:

Limited number of samples for evaluation:

We sampled 400 Reddit posts as our source data. This was because our eval method is rigorous and time-consuming, which was acknowledged and appreciated by Reviewers WuPB, Zhu4, and ArmX. The Reddit posts were sourced from 4 different subreddits, namely r/Anxiety, r/Anger, r/Parenting, and r/COVID19 support. We observe a clear difference between the topics in each domain, as shown by the topic modeling results in Table 5 (page 21), and this indicates the linguistic diversity of the samples. In addition, we also highlight the mentally-taxing and cognitively-demanding nature of devising oracle responses. To come up with a single response, the co-author of this study (who is a Ph.D. student in psychology) spent around 15 minutes with undivided attention.

Explorations for in-context learning:

We completely agree that in-context learning setups would be worthwhile to examine LLMs’ ability to provide cognitive reappraisals. We have explored prompting the language models with few-shot oracle examples, but we observe that the model would try to mimic the writing style from the examples, which makes the response more generic. We will include examples from in-context learning in the camera-ready version of the paper. Thank you for the suggestion!

Determining the order of the six appraisal dimensions:

In the current version of the paper, the order of the six appraisal dimensions is set according to the order they were arranged in Table 1. We will explore different orderings and their effects in future work.

Change the name in the example to “Carol”:

Thank you for the suggestion! We will update the name in the example from “Erin” to “Carol” in our camera-ready version of the paper.

We are grateful for your positive comments on our innovative RESORT framework that induces cognitive capabilities from LLMs, which Reviewers LP97 and Zhu4 also acknowledged. We are also thankful for your recognition of the in-depth expert evaluation carried out with psychologists holding advanced degrees, which Reviewers Zhu4 and ArmX also appreciated.

Uncomprehensive evaluation of LLMs:

While we agree that it is important to keep up with this rapidly growing field, it should be noted that at the time this paper was submitted (March 2024), GPT-4o was yet to be released and GPT-4 turbo was ranked first on the LMSYS Chatbot Arena Leaderboard. While we agree that evaluating RESORT under different systems would provide a more holistic view of current LLMs’ ability to offer cognitive reappraisals, we highlight that our evaluation was conducted on LLMs at different scales, and our findings suggest that RESORT effectively guides language models at the 7B scale. We also want to point out the rigorous and time-consuming nature of our expert evaluation method, which was appreciated by Reviewers Zhu4 and ArmX.

Closed-Sourced Data:

Ours will be the first open-sourced data of this kind: we will release our annotations and the LLM responses. We will of course first carefully anonymize the responses to preserve user privacy. As for the original posts, we will follow standard practices to provide links to them rather than releasing them.

Annotator Background Clarity:

We recruited 4 expert evaluators from UpWork, who hold either M.S. or Ph.D. degrees in psychology. The details of their background are elaborated in Section 5.2. The advanced expertise of our human evaluators allows for in-depth scrutiny of RESORT, which is acknowledged by Reviewers Zhu4 and ArmX.

Effectiveness of Iterative Guided Refinement:

We highlight that iterative guided refinement is always rated as the best-performing approach, even outperforming oracle responses, which is a key reason to accept this paper suggested by Reviewer ArmX. More importantly, in addition to the improvement in the metric “Alignment with Reappraisal Constitutions”, we also observe a consistent improvement in the level of perceived “Empathy” using iterative guided refinement.

Low inter-evaluator agreement on ALGN and EMPT:

The evaluation task is subjective by nature, although note that for ALGN and EMPT, our expert evaluators already have moderate agreement (Artstein & Poesio, 2008).