Revisiting the Self-correction Capability of Ethics in Small Large Language Models
Authors: Guangliang Liu, Zhiyu Xue, Rongrong Wang, Kristen Johnson


Reviewer paper summaries: This paper analyzes self-correction for removing social biases and stereotypes in LLM responses. Specifically, this work analyzes the relationships between model sizes and the moral self-correction capabilities of LLMs. They evaluate models with 355M (GPT-2) to 70B (Llama 2) parameters on social-bias benchmarks with multiple prompting strategies. Based on the experiments, the authors claim that the model scale lower bound for moral self-correction is 3.8B.
Summary of strengths: This paper targets the problem of mitigating social biases in LLM responses, which is essential for improving LLM safety.

The performance differences of different LLMs in moral self-correction is an important but relatively unexplored topic.
Summary of weaknesses: ### Analysis of the Number of Parameters.

As a major concern, this paper uses model sizes as a key parameter to analyze the capabilities of LLMs. It is widely known that the number of parameters is not the only factor influencing their capabilities. Even when the number of parameters is comparable, LLMs with different architectures or training procedures have largely different properties.

To provide a reliable analysis of the relationships between the model sizes and their capabilities, the authors at least need to provide the performances of multiple models with a similar number of parameters (e.g., 70B-class models of Llama 2 and 3, Qwen 1 and 2). Another possibility is to provide an experiment on models of different sizes that have identical architectures and training procedures (e.g., https://arxiv.org/abs/2212.08073).

Indeed, they mention, "Interestingly, the 3.8B model of Phi-3 outperforms all Llama-2 models, in both baseline performance and self-correction performance for BBQ" (Line 190). I think this is not surprising or interesting because Phi-3 is newer and trained with different training procedures. This result shows that model sizes should not be used as a key parameter for the analysis in their experiments.

In conclusion, I do not believe that this paper provides a reliable analysis to make the following claims about the relationships between the number of parameters and their capabilities.

* (Line 214) we can conclude that 214 LLMs, with less than 70B parameters, can not give 215 informative explanations based on their CoT capability w.r.t. morality-relevant questions
* (Line 199) the model scale threshold for the emergence of moral self-correction capability is 3.8B
Comments, suggestions and typos: ### Missing citations

Some popular papers on self-correction or self-feedback are missing in this paper. For example, the following papers also target harmfulness in LLM responses.

* Constitutional AI: Harmlessness from AI Feedback https://arxiv.org/abs/2212.08073
* REFINER: Reasoning Feedback on Intermediate Representations https://aclanthology.org/2024.eacl-long.67/

I recommend checking the survey papers in self-correction to provide a more comprehensive list of related papers in the introduction and Appendix A.1.

* Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00660/120911/Automatically-Correcting-Large-Language-Models
* When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs https://arxiv.org/abs/2406.01297
Overall assessment scores: 1.5