AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
- URL: http://arxiv.org/abs/2403.08978v1
- Date: Wed, 13 Mar 2024 22:06:03 GMT
- Title: AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
- Authors: Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, Honglak Lee,
- Abstract summary: AutoGuide bridges the knowledge gap in pre-trained LLMs by leveraging implicit knowledge in offline experiences.
We show that our approach outperforms competitive LLM-based baselines by a large margin in sequential decision-making benchmarks.
- Score: 74.17623527375241
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The primary limitation of large language models (LLMs) is their restricted understanding of the world. This poses significant difficulties for LLM-based agents, particularly in domains where pre-trained LLMs lack sufficient knowledge. In this paper, we introduce a novel framework, called AutoGuide, that bridges the knowledge gap in pre-trained LLMs by leveraging implicit knowledge in offline experiences. Specifically, AutoGuide effectively extracts knowledge embedded in offline data by extracting a set of state-aware guidelines. Importantly, each state-aware guideline is expressed in concise natural language and follows a conditional structure, clearly describing the state where it is applicable. As such, the resulting guidelines enable a principled way to provide helpful knowledge pertinent to an agent's current decision-making process. We show that our approach outperforms competitive LLM-based baselines by a large margin in sequential decision-making benchmarks.
Related papers
- SNAP: Unlearning Selective Knowledge in Large Language Models with Negative Instructions [37.172662930947446]
Instruction-following large language models (LLMs) inadvertently disclose personal or copyrighted information.
We propose SNAP, an innovative framework designed to selectively unlearn information.
We evaluate our framework on various NLP benchmarks and demonstrate that our approach retains the original LLM capabilities.
arXiv Detail & Related papers (2024-06-18T06:54:05Z) - 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) - KnowledgeNavigator: Leveraging Large Language Models for Enhanced
Reasoning over Knowledge Graph [11.808990571175269]
Large language model (LLM) has achieved outstanding performance on various downstream tasks with its powerful natural language understanding and zero-shot capability, but LLM still suffers from knowledge limitation.
We propose a novel framework KnowledgeNavigator to address these challenges by efficiently and accurately retrieving external knowledge from knowledge graph.
We evaluate KnowledgeNavigator on multiple public KGQA benchmarks, the experiments show the framework has great effectiveness and generalization.
arXiv Detail & Related papers (2023-12-26T04:22:56Z) - Knowledge Plugins: Enhancing Large Language Models for Domain-Specific
Recommendations [50.81844184210381]
We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
arXiv Detail & Related papers (2023-11-16T07:09:38Z) - LanguageMPC: Large Language Models as Decision Makers for Autonomous
Driving [87.1164964709168]
This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios.
Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
arXiv Detail & Related papers (2023-10-04T17:59:49Z) - Context-Aware Prompt Tuning for Vision-Language Model with
Dual-Alignment [15.180715595425864]
We introduce a novel method to improve the prompt learning of vision-language models by incorporating pre-trained large language models (LLMs)
With DuAl-PT, we propose to learn more context-aware prompts, benefiting from both explicit and implicit context modeling.
Empirically, DuAl-PT achieves superior performance on 11 downstream datasets on few-shot recognition and base-to-new generalization.
arXiv Detail & Related papers (2023-09-08T06:51:15Z) - Improving Open Information Extraction with Large Language Models: A
Study on Demonstration Uncertainty [52.72790059506241]
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text.
Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
arXiv Detail & Related papers (2023-09-07T01:35:24Z) - Augmented Large Language Models with Parametric Knowledge Guiding [72.71468058502228]
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities.
Their performance may be suboptimal for domain-specific tasks that require specialized knowledge due to limited exposure to the related data.
We propose the novel Parametric Knowledge Guiding (PKG) framework, which equips LLMs with a knowledge-guiding module to access relevant knowledge.
arXiv Detail & Related papers (2023-05-08T15:05:16Z)
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.