Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.14959 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909689650872320 |
|---|---|
| author | Zhang, Qingjie Wang, Di Qian, Haoting Li, Yiming Zhang, Tianwei Huang, Minlie Xu, Ke Li, Hewu Liu, Yan Qiu, Han |
| author_facet | Zhang, Qingjie Wang, Di Qian, Haoting Li, Yiming Zhang, Tianwei Huang, Minlie Xu, Ke Li, Hewu Liu, Yan Qiu, Han |
| contents | Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback prompts. In this paper, we aim to interpret LLMs' intrinsic self-correction for different tasks, especially for those failure cases. By including one simple task and three complex tasks with state-of-the-art (SOTA) LLMs like ChatGPT families (o1, 4o, 3.5-turbo) and Llama families (2-7B, 3-8B, and 3.1-8B), we design three interpretation methods to reveal the dark side of LLMs' intrinsic self-correction. We identify intrinsic self-correction can (1) cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions; (2) introduce human-like cognitive bias on complex tasks. In light of our findings, we also provide two simple yet effective strategies for alleviation: question repeating and supervised fine-tuning with a few samples. We open-source our work at https://x-isc.info/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14959 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding the Dark Side of LLMs' Intrinsic Self-Correction Zhang, Qingjie Wang, Di Qian, Haoting Li, Yiming Zhang, Tianwei Huang, Minlie Xu, Ke Li, Hewu Liu, Yan Qiu, Han Computation and Language Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback prompts. In this paper, we aim to interpret LLMs' intrinsic self-correction for different tasks, especially for those failure cases. By including one simple task and three complex tasks with state-of-the-art (SOTA) LLMs like ChatGPT families (o1, 4o, 3.5-turbo) and Llama families (2-7B, 3-8B, and 3.1-8B), we design three interpretation methods to reveal the dark side of LLMs' intrinsic self-correction. We identify intrinsic self-correction can (1) cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions; (2) introduce human-like cognitive bias on complex tasks. In light of our findings, we also provide two simple yet effective strategies for alleviation: question repeating and supervised fine-tuning with a few samples. We open-source our work at https://x-isc.info/. |
| title | Understanding the Dark Side of LLMs' Intrinsic Self-Correction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.14959 |