AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents

• URL: http://arxiv.org/abs/2403.08978v2
• Date: Tue, 03 Dec 2024 07:36:47 GMT
• Title: AutoGuide: Automated Generation and Selection of Context-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: We introduce a novel framework, called AutoGuide, which automatically generates context-aware guidelines from offline experiences.<n>As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process.<n>Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains.
• Score: 74.17623527375241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perform well in unfamiliar domains like web navigation, where they lack sufficient knowledge, has proven to be difficult with the demonstration-based in-context learning paradigm. In this paper, we introduce a novel framework, called AutoGuide, which addresses this limitation by automatically generating context-aware guidelines from offline experiences. Importantly, each context-aware guideline is expressed in concise natural language and follows a conditional structure, clearly describing the context where it is applicable. As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process, overcoming the limitations of the conventional demonstration-based learning paradigm. Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains, including real-world web navigation.

Related papers

• SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts [54.11162991206203]
  This paper consolidates diverse navigation tasks into a unified and generic framework. We propose a novel State-Adaptive Mixture of Experts (SAME) model that effectively enables an agent to infer decisions.
  arXiv  Detail & Related papers  (2024-12-07T06:12:53Z)
• RuAG: Learned-rule-augmented Generation for Large Language Models [62.64389390179651]
  We propose a novel framework, RuAG, to automatically distill large volumes of offline data into interpretable first-order logic rules. We evaluate our framework on public and private industrial tasks, including natural language processing, time-series, decision-making, and industrial tasks.
  arXiv  Detail & Related papers  (2024-11-04T00:01:34Z)
• KRAG Framework for Enhancing LLMs in the Legal Domain [0.48451657575793666]
  This paper introduces Knowledge Representation Augmented Generation (KRAG) KRAG is a framework designed to enhance the capabilities of Large Language Models (LLMs) within domain-specific applications. We present Soft PROLEG, an implementation model under KRAG, which uses inference graphs to aid LLMs in delivering structured legal reasoning.
  arXiv  Detail & Related papers  (2024-10-10T02:48:06Z)
• 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)
• RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model [22.25903116720301]
  explainability plays a critical role in trustworthy autonomous decision-making. Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent. We present RAG-Driver, a novel retrieval-augmented multi-modal large language model that leverages in-context learning for high-performance, explainable, and generalisable autonomous driving.
  arXiv  Detail & Related papers  (2024-02-16T16:57:18Z)
• DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton [44.26173742405563]
  This paper introduces the retrieval-augmented large language model with Definite Finite Automaton (DFA-RAG) DFA-RAG is a framework designed to enhance the capabilities of conversational agents using large language models (LLMs)
  arXiv  Detail & Related papers  (2024-02-06T21:14:45Z)
• 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)
• Generative Context-aware Fine-tuning of Self-supervised Speech Models [54.389711404209415]
  We study the use of generative large language models (LLM) generated context information. We propose an approach to distill the generated information during fine-tuning of self-supervised speech models. We evaluate the proposed approach using the SLUE and Libri-light benchmarks for several downstream tasks: automatic speech recognition, named entity recognition, and sentiment analysis.
  arXiv  Detail & Related papers  (2023-12-15T15:46:02Z)
• Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation [67.18144414660681]
  We propose a Fast-Slow Test-Time Adaptation (FSTTA) approach for online Vision-and-Language Navigation (VLN) Our method obtains impressive performance gains on four popular benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T07:47:39Z)
• Guideline Learning for In-context Information Extraction [29.062173997909028]
  In-context Information Extraction (IE) has recently garnered attention in the research community. We highlight a key reason for this shortfall: underspecified task description. We propose a Guideline Learning framework for In-context IE which reflectively learns and follows guidelines.
  arXiv  Detail & Related papers  (2023-10-08T08:25:16Z)
• 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)
• Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial [35.743576799998564]
  Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-aware computing. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning.
  arXiv  Detail & Related papers  (2023-09-24T00:15:39Z)
• 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)
• Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration [83.96729205383501]
  We introduce prompt-based learning to achieve fast adaptation for language embeddings. Our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE.
  arXiv  Detail & Related papers  (2022-03-08T11:01:24Z)
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts [46.03503882865222]
  AutoPrompt is an automated method to create prompts for a diverse set of tasks based on a gradient-guided search. We show that masked language models (MLMs) have an inherent capability to perform sentiment analysis and natural language inference without additional parameters or finetuning.
  arXiv  Detail & Related papers  (2020-10-29T22:54:00Z)

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.