Summary
The paper examines the causal and moral judgments made by large language models (LLMs) and their alignment with human intuitions. To do this, the researchers created a dataset of stories from 24 cognitive science papers, annotating each story with factors that influence people's judgments, such as norm violations and the avoidability or inevitability of harm.
The authors find that, on an aggregate level, the alignment between LLMs and human intuition has improved with newer models. However, statistical analyses reveal that LLMs and humans weigh these factors differently when making judgments.
Strengths
**Originality**: The paper presents an interesting approach to evaluating large language models (LLMs) by testing their ability to handle tasks related to causal judgments and moral permissibility. The authors have transcribed stories from various papers and used them to test the LLMs, focusing on several factors that influence people's causal judgments and moral dilemmas. This approach is original and provides a new perspective on the capabilities of LLMs.
**Quality**: The authors have meticulously transcribed stories from a number of papers, ensuring a wide range of scenarios for testing the LLMs. They have also collected responses for each story from a crowd-sourcing platform, ensuring a diverse set of responses for analysis.
**Clarity**: The paper is written in a clear and understandable manner. The authors have explained their methodology and the factors they focused on in a detailed and comprehensible way.
**Significance**: The paper contributes to understanding how LLMs handle complex tasks related to causality and morality. This is an important area of research, given the increasing use of LLMs in various applications. The insights gained from this study could be useful for improving these models in the future. The paper also opens up new avenues for research in this area.
Weaknesses
There are a few areas where it could be improved:
1. **Evaluating with more models**: The paper is a bit skewed towards OpenAI models (GPT3 and beyond) for the evaluation. Including more diverse models could provide a more comprehensive understanding of how different LLMs perform on the tasks. This could also help identify whether the observed behaviors are specific to these models or are more generally applicable to LLMs.
2. **Comparing with human performance**: While the paper does a good job of comparing the performance of different LLMs, it does not provide a clear comparison with human performance. This makes it difficult to assess how close the models are to human-level performance on these tasks. Including a human baseline could provide a more meaningful context for the results.
3. **Analyzing incorrect predictions**: The paper could benefit from a more detailed analysis of the models' incorrect predictions. This could help identify common patterns or biases in the models' errors, which could provide insights for improving the models.
4. **Generalizability of findings**: The paper's findings are based on a specific set of stories and tasks. It's unclear how generalizable these findings are to other tasks or domains. The authors could address this by testing the models on a wider range of tasks or by discussing the limitations of their approach in more detail.
Questions
1. **Evaluating with more models**: The paper primarily focuses on GPT-type models and its variants. Could the authors elaborate on why they chose to focus on these models? Would the inclusion of other language models provide different insights?
2. **Comparing with human performance**: The paper lacks a clear comparison with human performance. Could the authors provide a human baseline for these tasks? This could help in understanding how close the models are to achieving human-level performance.
3. **Analyzing incorrect predictions**: The paper could benefit from a more detailed analysis of the models' incorrect predictions. Could the authors provide more insights into the common patterns or biases in the models' errors? This could potentially help in improving the models.
4. **Generalizability of findings**: The findings of the paper are based on a specific set of stories and tasks. Could the authors discuss how generalizable these findings are to other tasks or domains?
5. **Interpretability and transparency**: The paper presents an analysis of how LLMs reason about causality and morality. However, it's not clear how these insights can be used to improve the interpretability and transparency of these models. Could the authors provide some thoughts on this?
Rating
7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Limitations
The paper addresses the limitations of the work and potential negative societal impact, although not in a dedicated section. The authors acknowledge that their focus is narrow and only on certain aspects of alignment with humans. They caution that their work should not be used to make sweeping and general statements about AI-human alignment. They also note that their moral permissibility task is not a certification task and should not be used as a flat benchmark to beat.
The authors also discuss the ethical considerations of their work, emphasizing the importance of assessing implicit intuitions underlying commonsense reasoning abilities in large language models (LLMs), especially in cases related to morality. They acknowledge that even if a model is not explicitly given the responsibility to make moral judgments, these judgments can appear across many forms of freely generated text. They also recognize the potential for replicating human biases in LLMs and state that this is something they would want to avoid.
In terms of potential negative societal impact, the authors don't explicitly discuss this. However, they do acknowledge the potential for misuse of LLMs and the ethical considerations that come with their use. They also discuss the importance of transparency and consent in their data collection process.