From Context to Action: Analysis of the Impact of State Representation and Context on the Generalization of Multi-Turn Web Navigation Agents

• URL: http://arxiv.org/abs/2410.23555v1
• Date: Thu, 31 Oct 2024 01:51:41 GMT
• Title: From Context to Action: Analysis of the Impact of State Representation and Context on the Generalization of Multi-Turn Web Navigation Agents
• Authors: Nalin Tiwary, Vardhan Dongre, Sanil Arun Chawla, Ashwin Lamani, Dilek Hakkani-Tür,
• Abstract summary: This study aims to analyze the various contextual elements crucial to the functioning of web navigation agents. We focus on the influence of interaction history and web page representation. Our work highlights improved agent performance across out-of-distribution scenarios.
• Score: 7.41862656697588
• License:
• Abstract: Recent advancements in Large Language Model (LLM)-based frameworks have extended their capabilities to complex real-world applications, such as interactive web navigation. These systems, driven by user commands, navigate web browsers to complete tasks through multi-turn dialogues, offering both innovative opportunities and significant challenges. Despite the introduction of benchmarks for conversational web navigation, a detailed understanding of the key contextual components that influence the performance of these agents remains elusive. This study aims to fill this gap by analyzing the various contextual elements crucial to the functioning of web navigation agents. We investigate the optimization of context management, focusing on the influence of interaction history and web page representation. Our work highlights improved agent performance across out-of-distribution scenarios, including unseen websites, categories, and geographic locations through effective context management. These findings provide insights into the design and optimization of LLM-based agents, enabling more accurate and effective web navigation in real-world applications.

Related papers

• AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents [52.13695464678006]
  This study enhances an LLM-based web agent by simply refining its observation and action space. AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively.
  arXiv  Detail & Related papers  (2024-10-17T17:50:38Z)
• Representing Web Applications As Knowledge Graphs [0.0]
  The proposed method models each node as a structured representation of the application's current state, with edges reflecting user-initiated actions or transitions. This structured representation enables a more comprehensive and functional understanding of web applications, offering valuable insights for downstream tasks such as automated testing and behavior analysis.
  arXiv  Detail & Related papers  (2024-10-06T02:50:41Z)
• A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine [14.123823081267336]
  This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN) The ACN framework consists of multiple specialized agents working collaboratively, each with distinct roles such as Account Manager, Solution Strategist, Information Manager, and Content Creator. A highlight of the ACN is the introduction of a Reflective Forward Optimization method (RFO), which supports the online synergistic adjustment among agents.
  arXiv  Detail & Related papers  (2024-09-01T07:01:22Z)
• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces. This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• AppAgent v2: Advanced Agent for Flexible Mobile Interactions [46.789563920416626]
  This work introduces a novel LLM-based multimodal agent framework for mobile devices. Our agent constructs a flexible action space that enhances adaptability across various applications. Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-05T06:31:39Z)
• WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? [83.19032025950986]
  We study the use of large language model-based agents for interacting with software via web browsers. WorkArena is a benchmark of 33 tasks based on the widely-used ServiceNow platform. BrowserGym is an environment for the design and evaluation of such agents.
  arXiv  Detail & Related papers  (2024-03-12T14:58:45Z)
• On the Multi-turn Instruction Following for Conversational Web Agents [83.51251174629084]
  We introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment. We propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques.
  arXiv  Detail & Related papers  (2024-02-23T02:18:12Z)
• VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks [93.85005277463802]
  VisualWebArena is a benchmark designed to assess the performance of multimodal web agents on realistic tasks. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives.
  arXiv  Detail & Related papers  (2024-01-24T18:35:21Z)
• AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models [2.234037966956278]
  We introduce AllTogether, a standardized prompt template that enhances task context representation. We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models.
  arXiv  Detail & Related papers  (2023-10-20T11:10:14Z)

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.