Summary of Metareflection: Learning Instructions For Language Agents Using Past Reflections, by Priyanshu Gupta et al.
MetaReflection: Learning Instructions for Language Agents using Past Reflections
by Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani, Arjun Radhakrishna, Sherry Shi, Gustavo Soares
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel offline reinforcement learning technique called MetaReflection, which enhances the performance of Large Language Models (LLMs) by augmenting a semantic memory based on experiential learnings from past trials. The authors demonstrate the efficacy of MetaReflection across multiple domains, including complex logical reasoning, biomedical semantic similarity, open-world question answering, and vulnerability threat detection. Compared to the raw GPT-4 baseline, MetaReflection boosts Language agents’ performance by 4% to 16.82%, performing on par with existing state-of-the-art prompt optimization techniques while requiring fewer LLM calls. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MetaReflection is a new way to improve Large Language Models (LLMs). These models are great at doing lots of tasks, but they can get stuck if they don’t do well enough. To help them, some people have tried different ways to make them better. But these methods only work sometimes and aren’t perfect. This paper shows how MetaReflection can be used to make LLMs better by helping them remember what they learned before. It tests this idea on lots of different tasks, like solving puzzles or finding answers to questions. The results show that MetaReflection makes the LLMs 4-17% better than usual. |
Keywords
» Artificial intelligence » Gpt » Optimization » Prompt » Question answering » Reinforcement learning
