In-Context Principle Learning from Mistakes

prompt-engineering
LEAP improves few-shot prompting for LLMs without needing more input or examples.
Author

Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra, Yiming Yang, Niket Tandon, Uri Alon

Published

February 8, 2024

Summary:

  • The article introduces a new approach called Learning Principles (LEAP) to address the limitations of in-context learning (ICL) and improve the performance of language models across various reasoning tasks. LEAP induces the model to make mistakes, reflect on them, and learn explicit task-specific “principles” from these mistakes without human supervision. The results demonstrate the effectiveness of LEAP in modifying the correctness of responses across a range of tasks.

Major Findings:

  1. LEAP improves the performance of strong language models across different reasoning tasks.
  2. LEAP has the potential to outperform baseline approaches in certain cases, particularly in tasks such as temporal sequences and snarks.
  3. The principles learned by LEAP emphasize clarity, conciseness, relevance, uniqueness, accuracy, engagement, and understanding of context, contributing to the overall effectiveness of the system in generating responses.

Analysis and Critique:

  • The significance of LEAP lies in its ability to improve the performance of language models across various reasoning tasks without requiring additional input or examples. However, the limitations of LEAP with open-source models and the need for strong base models for effective use are highlighted. The comparison between few-shot and zero-shot results underscores the potential of LEAP methods in enhancing the performance of large language models across different types of reasoning tasks. The section on pronoun ambiguity and the principles and prompts for LEAP provide valuable insights into the impact of LEAP on response correctness and the systematic approach of LEAP in learning from mistakes and improving reasoning performance. However, areas needing further methodological refinement are also identified, suggesting the need for continued improvement in specific task areas.

Appendix

Model gpt-3.5-turbo-1106
Date Generated 2024-02-26
Abstract https://arxiv.org/abs/2402.05403v1
HTML https://browse.arxiv.org/html/2402.05403v1
Truncated True
Word Count 29440