@inproceedings{liu-etal-2025-smaller,
title = "Smaller Large Language Models Can Do Moral Self-Correction",
author = "Liu, Guangliang and
Xue, Zhiyu and
Zhang, Xitong and
Wang, Rongrong and
Johnson, Kristen",
editor = "Cao, Trista and
Das, Anubrata and
Kumarage, Tharindu and
Wan, Yixin and
Krishna, Satyapriya and
Mehrabi, Ninareh and
Dhamala, Jwala and
Ramakrishna, Anil and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul and
Chang, Kai-Wei",
booktitle = "Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.trustnlp-main.5/",
doi = "10.18653/v1/2025.trustnlp-main.5",
pages = "56--65",
ISBN = "979-8-89176-233-6",
abstract = "Self-correction is one of the most amazing emerging capabilities of Large Language Models (LLMs), enabling LLMs to self-modify an inappropriate output given a natural language feedback which describes the problems of that output. Moral self-correction is a post-hoc approach correcting unethical generations without requiring a gradient update, making it both computationally lightweight and capable of preserving the language modeling ability. Previous works have shown that LLMs can self-debias, and it has been reported that small models, i.e., those with less than 22B parameters, are \textit{not} capable of moral self-correction.However, there is no direct proof as to why such smaller models fall short of moral self-correction, though previous research hypothesizes that larger models are skilled in following instructions and understanding abstract social norms.In this paper, we empirically validate this hypothesis in the context of social stereotyping, through meticulous prompting.Our experimental results indicate that \textbf{(i)} surprisingly, 3.8B LLMs with proper safety alignment fine-tuning can achieve very good moral self-correction performance, highlighting the significant effects of safety alignment; and \textbf{(ii)} small LLMs are indeed weaker than larger-scale models in terms of comprehending social norms and self-explanation through CoT, but all scales of LLMs show bad self-correction performance given unethical instructions."
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<abstract>Self-correction is one of the most amazing emerging capabilities of Large Language Models (LLMs), enabling LLMs to self-modify an inappropriate output given a natural language feedback which describes the problems of that output. Moral self-correction is a post-hoc approach correcting unethical generations without requiring a gradient update, making it both computationally lightweight and capable of preserving the language modeling ability. Previous works have shown that LLMs can self-debias, and it has been reported that small models, i.e., those with less than 22B parameters, are not capable of moral self-correction.However, there is no direct proof as to why such smaller models fall short of moral self-correction, though previous research hypothesizes that larger models are skilled in following instructions and understanding abstract social norms.In this paper, we empirically validate this hypothesis in the context of social stereotyping, through meticulous prompting.Our experimental results indicate that (i) surprisingly, 3.8B LLMs with proper safety alignment fine-tuning can achieve very good moral self-correction performance, highlighting the significant effects of safety alignment; and (ii) small LLMs are indeed weaker than larger-scale models in terms of comprehending social norms and self-explanation through CoT, but all scales of LLMs show bad self-correction performance given unethical instructions.</abstract>
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%0 Conference Proceedings
%T Smaller Large Language Models Can Do Moral Self-Correction
%A Liu, Guangliang
%A Xue, Zhiyu
%A Zhang, Xitong
%A Wang, Rongrong
%A Johnson, Kristen
%Y Cao, Trista
%Y Das, Anubrata
%Y Kumarage, Tharindu
%Y Wan, Yixin
%Y Krishna, Satyapriya
%Y Mehrabi, Ninareh
%Y Dhamala, Jwala
%Y Ramakrishna, Anil
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%Y Chang, Kai-Wei
%S Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-233-6
%F liu-etal-2025-smaller
%X Self-correction is one of the most amazing emerging capabilities of Large Language Models (LLMs), enabling LLMs to self-modify an inappropriate output given a natural language feedback which describes the problems of that output. Moral self-correction is a post-hoc approach correcting unethical generations without requiring a gradient update, making it both computationally lightweight and capable of preserving the language modeling ability. Previous works have shown that LLMs can self-debias, and it has been reported that small models, i.e., those with less than 22B parameters, are not capable of moral self-correction.However, there is no direct proof as to why such smaller models fall short of moral self-correction, though previous research hypothesizes that larger models are skilled in following instructions and understanding abstract social norms.In this paper, we empirically validate this hypothesis in the context of social stereotyping, through meticulous prompting.Our experimental results indicate that (i) surprisingly, 3.8B LLMs with proper safety alignment fine-tuning can achieve very good moral self-correction performance, highlighting the significant effects of safety alignment; and (ii) small LLMs are indeed weaker than larger-scale models in terms of comprehending social norms and self-explanation through CoT, but all scales of LLMs show bad self-correction performance given unethical instructions.
%R 10.18653/v1/2025.trustnlp-main.5
%U https://aclanthology.org/2025.trustnlp-main.5/
%U https://doi.org/10.18653/v1/2025.trustnlp-main.5
%P 56-65
Markdown (Informal)
[Smaller Large Language Models Can Do Moral Self-Correction](https://aclanthology.org/2025.trustnlp-main.5/) (Liu et al., TrustNLP 2025)
ACL
- Guangliang Liu, Zhiyu Xue, Xitong Zhang, Rongrong Wang, and Kristen Johnson. 2025. Smaller Large Language Models Can Do Moral Self-Correction. In Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025), pages 56–65, Albuquerque, New Mexico. Association for Computational Linguistics.