Summary Self-Correction for LLMs Mistake Finding and Correction arxiv.org
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The paper examines the self-correction abilities of Large Language Models (LLMs) and finds that they have difficulty identifying mistakes, but backtracking can effectively correct incorrect outputs without impacting correct ones.
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Key Points
- Large Language Models (LLMs) have limited self-correction capabilities in terms of identifying and correcting logical errors.
- Mistake finding is a challenging task for LLMs, as they struggle to identify logical mistakes.
- The authors propose a backtracking method that uses mistake location information to improve LLM performance in output correction.
- Backtracking is shown to effectively correct incorrect outputs without significantly affecting correct outputs.
- Prompting for mistake location alone is not a reliable strategy for determining correctness in LLMs.
- Backtracking can correct logical errors in Chain-of-Thought reasoning traces, even without gold standard labels.
- Further research is needed to evaluate backtracking on a larger scale and in more realistic settings.
- The potential of using reward models for mistake finding and the transferability of learning to find mistakes in different tasks is highlighted.
Summaries
24 word summary
This paper evaluates Large Language Models' (LLMs) self-correction abilities, finding they struggle with mistake finding. Backtracking effectively corrects incorrect outputs without affecting correct ones.
66 word summary
This paper examines the self-correction abilities of Large Language Models (LLMs). It evaluates LLMs on a dataset of logical mistakes and finds that LLMs struggle with mistake finding. The authors propose a backtracking method that effectively corrects incorrect outputs without affecting correct ones. They also investigate using mistake location as a proxy for correctness and discuss the limitations and potential for further research in evaluating backtracking.
155 word summary
This paper examines the self-correction abilities of Large Language Models (LLMs). It introduces the concept of mistake finding and output correction as two components of the self-correction process. The authors evaluate various LLMs on a dataset of logical mistakes called BIG-Bench Mistake and find that LLMs struggle with mistake finding. They propose a backtracking method that uses mistake location information to improve output correction. The method effectively corrects originally incorrect outputs without significantly affecting originally correct ones. The paper also investigates using mistake location as a proxy for correctness and determines that it is not a reliable strategy. The authors demonstrate the effectiveness of backtracking with gold mistake location labels and simulated reward models. They conclude by discussing the limitations of their dataset and the potential for further research in evaluating backtracking on a larger scale. Overall, the paper contributes to understanding LLMs' self-correction capabilities and the potential use of reward models in the process.
384 word summary
This paper focuses on the self-correction capabilities of Large Language Models (LLMs). While previous research has shown promise in improving LLM outputs in terms of style and quality, there is limited evidence that LLMs can identify and correct their own reasoning and logical errors without external feedback. To address this, the authors break down the self-correction process into two components: mistake finding and output correction.
For mistake finding, the authors introduce BIG-Bench Mistake, a dataset of logical mistakes in Chain-of-Thought reasoning traces. They evaluate several state-of-the-art LLMs on this dataset and find that LLMs generally struggle with finding logical mistakes. This highlights the need for further improvements in mistake finding.
For output correction, the authors propose a backtracking method that uses information about mistake location to improve performance. They demonstrate that this method can correct outputs that are originally incorrect, with minimal effect on outputs that are originally correct. The backtracking method is seen as a lightweight alternative to reinforcement learning methods and remains effective with a reward model at 60-70% accuracy.
The paper also explores the concept of using mistake location as a proxy for correctness. They investigate whether LLMs can reliably determine the correctness of a trace based on mistake location alone. The results show that prompting for mistake location is a poor strategy for determining correctness, as the weighted average F1 scores are lower than a baseline that predicts all traces as incorrect.
In addition, the authors conduct experiments to evaluate the effectiveness of backtracking. They show that backtracking with gold mistake location labels can correct logical errors in CoT traces. They also explore the use of simulated reward models and demonstrate that backtracking is still effective even without gold standard labels.
The paper concludes by discussing the limitations of their dataset and the need for further research to evaluate backtracking on a larger scale and in more realistic settings. They also highlight the potential of using dedicated reward models for mistake finding and the transferability of learning to find mistakes in out-of-distribution tasks.
Overall, this paper provides insights into the self-correction capabilities of LLMs and proposes a backtracking method for correcting logical errors. The findings contribute to the understanding of LLMs' ability to identify and correct their own mistakes, and the potential for using reward models in the self-correction process.