Conceitos essenciais
Self-Contrast strategy enhances LLM reflection by exploring diverse solving perspectives and contrasting differences to improve accuracy and stability.
Resumo
The article discusses the challenges faced by Large Language Models (LLMs) in self-reflection and proposes the Self-Contrast strategy to improve reflection accuracy and stability. It explores the importance of diverse solving perspectives, contrasts differences between responses, and generates checklists for re-examination. Experiments show significant improvements in mathematical reasoning and translation tasks across various LLMs.
- Introduction
- Importance of reasoning and decision-making for artificial general intelligence.
- Impressive capabilities of LLMs in various domains.
- Investigation
- Challenges in LLMs' self-reflection without external feedback.
- Intrinsic reflection limitations and feedback analysis.
- Self-Contrast Strategy
- Creating diverse solving perspectives.
- Contrasting differences between responses.
- Generating checklists for reflection.
- Experiments
- Evaluation of Self-Contrast against baselines in mathematical reasoning and translation tasks.
- Comparison of different components of Self-Contrast.
- Conclusion
- Self-Contrast significantly improves reflection accuracy and stability in LLMs.
Estatísticas
"For a time, this belief appeared to dominate the community."
"LLMs often provide two unexpected feedback: 1) Overconfidence (46.7%): Stubbornly insisting that the previous solution is correct. 2) Inconsistency (45.7%): The feedback is highly inconsistent when self-evaluating the same response multiple times."
"Across various LLMs and tasks, the performance gains from reflection are not significant, and occasionally detrimental."
Citações
"Our investigation unveils that the key bottleneck is the quality of the self-evaluated feedback."
"Self-Contrast can mitigate biases introduced by specific prompts."