MetaReflection: Learning Instructions for Language Agents using Past Reflections

• URL: http://arxiv.org/abs/2405.13009v2
• Date: Thu, 10 Oct 2024 11:51:24 GMT
• Title: MetaReflection: Learning Instructions for Language Agents using Past Reflections
• Authors: Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani, Arjun Radhakrishna, Sherry Shi, Gustavo Soares,
• Abstract summary: We introduce MetaReflection, a novel offline reinforcement learning technique that enhances the performance of Language Agents. We demonstrate the efficacy of MetaReflection by evaluating across multiple domains, including complex logical reasoning, biomedical semantic similarity, open world question answering, and vulnerability threat detection.
• Score: 11.028256182234017
• License:
• Abstract: The popularity of Large Language Models (LLMs) have unleashed a new age ofLanguage Agents for solving a diverse range of tasks. While contemporary frontier LLMs are capable enough to power reasonably good Language agents, the closed-API model makes it hard to improve in cases they perform sub-optimally. To address this, recent works have explored ways to improve their performance using techniques like self-reflection and prompt optimization. Unfortunately, techniques like self-reflection can be used only in an online setup, while contemporary prompt optimization techniques are designed and tested to work on simple tasks. To this end, we introduce MetaReflection, a novel offline reinforcement learning technique that enhances the performance of Language Agents by augmenting a semantic memory based on experiential learnings from past trials. We demonstrate the efficacy of MetaReflection by evaluating across multiple domains, including complex logical reasoning, biomedical semantic similarity, open world question answering, and vulnerability threat detection, in Infrastructure-as-Code, spanning different agent designs. MetaReflection boosts Language agents' performance by 4% to 16.82% over the raw GPT-4 baseline and performs on par with existing state-of-the-art prompt optimization techniques while requiring fewer LLM calls.

Related papers

• RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models [5.0741409008225755]
  Large language models (LLMs) have emerged as promising tools for solving challenging robotic tasks. Most existing LLM-based agents lack the ability to retain and learn from past interactions. We propose RAG-Modulo, a framework that enhances LLM-based agents with a memory of past interactions and incorporates critics to evaluate the agents' decisions.
  arXiv  Detail & Related papers  (2024-09-18T20:03:32Z)
• VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs [38.03704123835915]
  Large-scale generative language and vision-language models excel in in-context learning for decision making. We propose In-Context Abstraction Learning (ICAL), a method that builds a memory of multimodal experience from sub-optimal demonstrations and human feedback. Our approach significantly reduces reliance on manual prompt engineering and consistently outperforms in-context learning from action plans that lack such abstractions.
  arXiv  Detail & Related papers  (2024-06-20T17:45:02Z)
• Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs [15.911445732909849]
  Large language models (LLMs) are at the forefront of transforming numerous domains globally. However, their inclusivity and effectiveness remain limited for non-Latin scripts and low-resource languages. This paper tackles the imperative challenge of enhancing the multilingual performance of LLMs without extensive training or fine-tuning.
  arXiv  Detail & Related papers  (2024-05-28T16:56:42Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts. We identify two pivotal factors in model parameter learning: update direction and update method. In particular, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• Are Large Language Models Good Prompt Optimizers? [65.48910201816223]
  We conduct a study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLMs struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge. We introduce a new "Automatic Behavior Optimization" paradigm, which directly optimize the target model's behavior in a more controllable manner.
  arXiv  Detail & Related papers  (2024-02-03T09:48:54Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization [62.0397906276669]
  CLIN is the first language-based agent to continually improve over multiple trials. It can improve its zero-shot performance by 4 points (13 for new tasks) and can further improve performance there through continual memory updates. This suggests a new architecture for agents built on frozen models that can still continually and rapidly improve over time.
  arXiv  Detail & Related papers  (2023-10-16T07:17:27Z)
• Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization [103.70896967077294]
  This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model. Our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model. Experimental results on various tasks demonstrate that the language agents improve over time.
  arXiv  Detail & Related papers  (2023-08-04T06:14:23Z)
• Reflexion: Language Agents with Verbal Reinforcement Learning [44.85337947858337]
  Reflexion is a novel framework to reinforce language agents not by updating weights, but through linguistic feedback. It is flexible enough to incorporate various types (scalar values or free-form language) and sources (external or internally simulated) of feedback signals. For example, Reflexion achieves a 91% pass@1 accuracy on the HumanEval coding benchmark, surpassing the previous state-of-the-art GPT-4 that achieves 80%.
  arXiv  Detail & Related papers  (2023-03-20T18:08:50Z)
• LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models [67.19124099815645]
  We propose a novel Language-Aware Soft Prompting (LASP) learning method to alleviate base class overfitting. LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available. LASP matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets.
  arXiv  Detail & Related papers  (2022-10-03T17:56:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.