ExpeL: LLM Agents Are Experiential Learners

• URL: http://arxiv.org/abs/2308.10144v2
• Date: Mon, 18 Dec 2023 03:11:52 GMT
• Title: ExpeL: LLM Agents Are Experiential Learners
• Authors: Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, Gao Huang
• Abstract summary: We introduce the Experiential Learning (ExpeL) agent to allow learning from agent experiences without requiring parametric updates. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions.
• Score: 60.54312035818746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, finetuning them for specific tasks is resource-intensive and may diminish the model's generalization capabilities. Moreover, state-of-the-art language models like GPT-4 and Claude are primarily accessible through API calls, with their parametric weights remaining proprietary and unavailable to the public. This scenario emphasizes the growing need for new methodologies that allow learning from agent experiences without requiring parametric updates. To address these problems, we introduce the Experiential Learning (ExpeL) agent. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions. Our empirical results highlight the robust learning efficacy of the ExpeL agent, indicating a consistent enhancement in its performance as it accumulates experiences. We further explore the emerging capabilities and transfer learning potential of the ExpeL agent through qualitative observations and additional experiments.

Related papers

• Empowering Large Language Model Agents through Action Learning [85.39581419680755]
  Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error. We argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents. We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions.
  arXiv  Detail & Related papers  (2024-02-24T13:13:04Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Augmenting LLMs with Knowledge: A survey on hallucination prevention [0.0]
  This survey delves into the realm of language models (LMs) augmented with the ability to tap into external knowledge sources. While adhering to the standard objective of predicting missing tokens, these augmented LMs leverage diverse, possibly non-parametric external modules.
  arXiv  Detail & Related papers  (2023-09-28T14:09:58Z)
• Thrust: Adaptively Propels Large Language Models with External Knowledge [58.72867916604562]
  Large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters. The inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. We propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary.
  arXiv  Detail & Related papers  (2023-07-19T20:16:46Z)
• Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis [4.055489363682198]
  Large language models (LLMs) offer significant promise as a knowledge source for task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LLM, but alone it is insufficient for acquiring relevant, situationally grounded knowledge for an embodied agent learning novel tasks. We describe a cognitive-agent approach, STARS, that extends and complements prompt engineering, mitigating its limitations and thus enabling an agent to acquire new task knowledge matched to its native language capabilities, embodiment, environment, and user preferences.
  arXiv  Detail & Related papers  (2023-06-11T20:50:14Z)
• Asking Before Acting: Gather Information in Embodied Decision Making with Language Models [20.282749796376063]
  We show that Large Language Models (LLMs) encounter challenges in efficiently gathering essential information in unfamiliar environments. We propose textitAsking Before Acting (ABA), a method that empowers the agent to proactively inquire with external sources for pertinent information using natural language. We conduct extensive experiments involving a spectrum of environments including text-based household everyday tasks, robot arm manipulation tasks, and real world open domain image based embodied tasks.
  arXiv  Detail & Related papers  (2023-05-25T04:05:08Z)
• Knowledge Rumination for Pre-trained Language Models [77.55888291165462]
  We propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize related latent knowledge without retrieving it from the external corpus. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, and GPT-3.
  arXiv  Detail & Related papers  (2023-05-15T15:47:09Z)
• Improving Language Model Prompting in Support of Semi-autonomous Task Learning [6.021787236982658]
  Language models (LLMs) offer potential as a source of knowledge for agents that need to acquire new task competencies. We describe efforts toward a novel agent capability that can construct cues that result in useful LLM responses for an agent learning a new task.
  arXiv  Detail & Related papers  (2022-09-13T15:36:01Z)

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.