Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents

• URL: http://arxiv.org/abs/2502.15690v1
• Date: Fri, 20 Dec 2024 08:03:12 GMT
• Title: Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents
• Authors: Chuanrui Hu, Shichong Xie, Baoxin Wang, Bin Chen, Xiaofeng Cong, Jun Zhang,
• Abstract summary: Large language models (LLMs), adopted to understand human language, drive the development of artificial intelligence (AI) web search agents.<n>We propose a general-purpose and training-free web search agent by level-aware navigation, Level-Navi Agent, accompanied by a well-annotated dataset (Web24) and a suitable evaluation metric.
• Score: 9.003325286793288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs), adopted to understand human language, drive the development of artificial intelligence (AI) web search agents. Compared to traditional search engines, LLM-powered AI search agents are capable of understanding and responding to complex queries with greater depth, enabling more accurate operations and better context recognition. However, little attention and effort has been paid to the Chinese web search, which results in that the capabilities of open-source models have not been uniformly and fairly evaluated. The difficulty lies in lacking three aspects: an unified agent framework, an accurately labeled dataset, and a suitable evaluation metric. To address these issues, we propose a general-purpose and training-free web search agent by level-aware navigation, Level-Navi Agent, accompanied by a well-annotated dataset (Web24) and a suitable evaluation metric. Level-Navi Agent can think through complex user questions and conduct searches across various levels on the internet to gather information for questions. Meanwhile, we provide a comprehensive evaluation of state-of-the-art LLMs under fair settings. To further facilitate future research, source code is available at Github.

Related papers

• BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese [30.994503814617637]
  BrowseComp-ZH is a benchmark for evaluating large language models (LLMs) on the Chinese web. It consists of 289 multi-hop questions spanning 11 diverse domains. Despite strong conversational and retrieval capabilities, most models struggle severely.
  arXiv  Detail & Related papers  (2025-04-27T17:32:43Z)
• A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval [26.797683195019246]
  Large language models (LLMs) have demonstrated capabilities that surpass human performance in various language-related tasks.<n>This paper explores the transformative potential of large language model agents in enhancing search and recommendation systems.
  arXiv  Detail & Related papers  (2025-03-07T18:20:30Z)
• AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials [53.376263056033046]
  Existing approaches rely on expensive human annotation, making them unsustainable at scale.<n>We propose AgentTrek, a scalable data synthesis pipeline that generates web agent trajectories by leveraging publicly available tutorials.<n>Our fully automated approach significantly reduces data collection costs, achieving a cost of just $0.55 per high-quality trajectory without human annotators.
  arXiv  Detail & Related papers  (2024-12-12T18:59:27Z)
• 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)
• MindSearch: Mimicking Human Minds Elicits Deep AI Searcher [20.729251584466983]
  We introduce MindSearch to mimic the human minds in web information seeking and integration. The framework can be instantiated by a simple yet effective LLM-based multi-agent framework. MindSearch demonstrates significant improvement in the response quality in terms of depth and breadth.
  arXiv  Detail & Related papers  (2024-07-29T17:12:40Z)
• Tree Search for Language Model Agents [69.43007235771383]
  We propose an inference-time search algorithm for LM agents to perform exploration and multi-step planning in interactive web environments. Our approach is a form of best-first tree search that operates within the actual environment space. It is the first tree search algorithm for LM agents that shows effectiveness on realistic web tasks.
  arXiv  Detail & Related papers  (2024-07-01T17:07:55Z)
• Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
  This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution. Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy. In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases [93.96463520716759]
  We develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Knowledge Bases. Our benchmark covers three domains: product search, academic paper search, and queries in precision medicine. We design a novel pipeline to synthesize realistic user queries that integrate diverse relational information and complex textual properties.
  arXiv  Detail & Related papers  (2024-04-19T22:54:54Z)
• 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)
• KwaiAgents: Generalized Information-seeking Agent System with Large Language Models [33.59597020276034]
  Humans excel in critical thinking, planning, reflection, and harnessing available tools to interact with and interpret the world. Recent advancements in large language models (LLMs) suggest that machines might also possess the aforementioned human-like capabilities. We introduce KwaiAgents, a generalized information-seeking agent system based on LLMs.
  arXiv  Detail & Related papers  (2023-12-08T08:11:11Z)
• Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models [31.509994889286183]
  We introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of language models (LMs) in reasoning, acting, and planning. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism. LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT
  arXiv  Detail & Related papers  (2023-10-06T17:55:11Z)

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.