Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis

• URL: http://arxiv.org/abs/2306.06770v4
• Date: Tue, 20 Feb 2024 14:34:14 GMT
• Title: Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis
• Authors: James R. Kirk, Robert E. Wray, Peter Lindes, John E. Laird
• Abstract summary: 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.
• Score: 4.055489363682198
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. The STARS approach is to increase the response space of LLMs and deploy general strategies, embedded within the autonomous agent, to evaluate, repair, and select among candidate responses produced by the LLM. We describe the approach and experiments that show how an agent, by retrieving and evaluating a breadth of responses from the LLM, can achieve 77-94% task completion in one-shot learning without user oversight. The approach achieves 100% task completion when human oversight (such as an indication of preference) is provided. Further, the type of oversight largely shifts from explicit, natural language instruction to simple confirmation/discomfirmation of high-quality responses that have been vetted by the agent before presentation to a user.

Related papers

• Automating Knowledge Discovery from Scientific Literature via LLMs: A Dual-Agent Approach with Progressive Ontology Prompting [59.97247234955861]
  We introduce a novel framework based on large language models (LLMs) that combines a progressive prompting algorithm with a dual-agent system, named LLM-Duo. Our method identifies 2,421 interventions from 64,177 research articles in the speech-language therapy domain.
  arXiv  Detail & Related papers  (2024-08-20T16:42:23Z)
• Visual Grounding for Object-Level Generalization in Reinforcement Learning [35.39214541324909]
  Generalization is a pivotal challenge for agents following natural language instructions. We leverage a vision-language model (VLM) for visual grounding and transfer its vision-language knowledge into reinforcement learning. We show that our intrinsic reward significantly improves performance on challenging skill learning.
  arXiv  Detail & Related papers  (2024-08-04T06:34:24Z)
• WESE: Weak Exploration to Strong Exploitation for LLM Agents [95.6720931773781]
  This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks. WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task.
  arXiv  Detail & Related papers  (2024-04-11T03:31:54Z)
• KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents [54.09074527006576]
  Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges. This inadequacy primarily stems from the lack of built-in action knowledge in language agents. We introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit action knowledge.
  arXiv  Detail & Related papers  (2024-03-05T16:39:12Z)
• 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)
• Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents [16.24662355253529]
  Large Language Models (LLMs) can address sequential decision-making tasks through the provision of high-level instructions. LLMs lack specialization in tackling specific target problems, particularly in real-time dynamic environments. We introduce a novel framework that addresses these challenges by training a smaller, specialized student RL agent using instructions from an LLM-based teacher agent.
  arXiv  Detail & Related papers  (2023-11-22T13:15:42Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• ExpeL: LLM Agents Are Experiential Learners [60.54312035818746]
  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.
  arXiv  Detail & Related papers  (2023-08-20T03:03:34Z)
• 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.