| <｜begin▁of▁sentence｜><｜User｜> | |
| ## Tools | |
| You have access to a set of tools you can use to answer the user's question. | |
| You can invoke functions by writing a "<｜DSML｜function_calls>" block like the following as part of your reply to the user: | |
| <｜DSML｜function_calls> | |
| <｜DSML｜invoke name="$FUNCTION_NAME"> | |
| <｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter> | |
| ... | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="$FUNCTION_NAME2"> | |
| ... | |
| </｜DSML｜invoke> | |
| </｜DSML｜function_calls> | |
| String and scalar parameters should be specified as is without any escaping or quotes, while lists and objects should use JSON format. The "string" attribute should be set to "true" for string type parameters and "false" for other types (numbers, booleans, arrays, objects). | |
| If the thinking_mode is enabled, then after function results you should strongly consider outputting a thinking block. Here is an example: | |
| <｜DSML｜function_calls> | |
| ... | |
| </｜DSML｜function_calls> | |
| <function_results> | |
| ... | |
| </function_results> | |
| <think>...thinking about results</think> | |
| Here are the functions available in JSONSchema format: | |
| <functions> | |
| {"name": "search", "description": "Searches for information related to query and displays topn results.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "The search query string"}, "topn": {"type": "integer", "description": "Number of top results to display", "default": 10}, "source": {"type": "string", "description": "Source to search within", "enum": ["web", "news"], "default": "web"}}, "required": ["query"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}} | |
| {"name": "open", "description": "Opens the link id from the page indicated by cursor starting at line number loc, showing num_lines lines. Valid link ids are displayed with the formatting: 【{id}†.*】. If cursor is not provided, the most recent page is implied. If id is a string, it is treated as a fully qualified URL associated with source. If loc is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available. Use this function without id to scroll to a new location of an opened page.", "parameters": {"type": "object", "properties": {"id": {"description": "Link ID (number) or fully qualified URL (string)", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "cursor": {"type": "integer", "description": "Page cursor indicator", "default": -1}, "loc": {"type": "integer", "description": "Starting line number", "default": -1}, "num_lines": {"type": "integer", "description": "Number of lines to show", "default": -1}, "view_source": {"type": "boolean", "description": "Whether to view source", "default": false}, "source": {"type": "string", "description": "Source associated with the URL", "enum": ["web", "news"], "default": "web"}}, "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}} | |
| {"name": "find", "description": "Finds exact matches of pattern in the current page, or the page given by cursor.", "parameters": {"type": "object", "properties": {"pattern": {"type": "string", "description": "The exact pattern to search for"}, "cursor": {"type": "integer", "description": "Page cursor to search in", "default": -1}}, "required": ["pattern"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}} | |
| </functions> | |
| # The user's message is: 帮我调研一下，目前有哪些针对search agent的benchmark？详细介绍各自的特点、使用场景、例题。 | |
| <system-reminder> | |
| ## Today’s Date | |
| 2025-11-27, Thursday. | |
| </system-reminder><｜Assistant｜><think>用户想调研目前针对搜索智能体（search agent）的基准测试（benchmark），要求详细介绍各自的特点、使用场景和例题。我需要提供一份全面的概述，涵盖现有的基准测试，例如WebGPT、WebShop、ALFWorld、HotpotQA、FEVER、TriviaQA、Natural Questions、SearchQA、MS MARCO、BEIR、LoTTE、MIRACL、Zero-shot IR基准等。同时，需要区分这些基准测试是针对检索任务、问答任务，还是具体针对搜索智能体的评估。此外，还应考虑那些专门为评估搜索智能体（即能够与环境交互、执行搜索、浏览网页、使用工具的智能体）而设计的基准测试，例如WebGPT、WebShop、ALFWorld、Mind2Web、WebArena、VisualWebArena、SearchBench、WebVLN、WebNav等。我需要详细介绍每个基准测试的特点、使用场景，并举例说明。为了确保信息的全面性和时效性，我将同时进行两项搜索：一项是针对“search agent benchmark”的通用搜索，以获取广泛的基准测试信息；另一项是针对“搜索智能体 基准测试”的中文搜索，以获取更符合用户语言习惯和可能更具体的中文资料。</think> | |
| <｜DSML｜function_calls> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">search agent benchmark 2024</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">搜索智能体 基准测试</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| </｜DSML｜function_calls><｜end▁of▁sentence｜> | |
| <function_results> | |
| <result>[0] search agent benchmark 2024 (Search_Results/search agent benchmark 2024) | |
| **viewing lines [0 - 39] of 39** | |
| L0: | |
| L1: URL: Search_Results/search agent benchmark 2024 | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†HSCodeComp: A Realistic and Expert-level Benchmark for ...; publish_date: | |
| L5: none†arxiv.org】 Oct 22, 2025 — To fill this gap, we introduce HSCodeComp, the | |
| L6: first realistic, expert-level e-commerce benchmark designed to evaluate deep | |
| L7: search agents in ... | |
| L8: * 【1†open-compass/GTA - A Benchmark for General Tool Agents; publish_date: | |
| L9: none†github.com】 GTA is a benchmark to evaluate the tool-use capability of LLM- | |
| L10: based agents in real-world scenarios. It features three main aspects. | |
| L11: * 【2†Benchmarking real-time trust scoring across five AI Agent ...; | |
| L12: publish_date: none†cleanlab.ai】 Aug 20, 2025 — This article evaluates 5 AI Agent | |
| L13: architectures over the BOLAA (ICLR 2024) benchmark, and assesses the effects of | |
| L14: adding automated trust ... | |
| L15: * 【3†10 AI agent benchmarks; publish_date: none†www.evidentlyai.com】 Jul 11, | |
| L16: 2025 — We put together 10 AI agent benchmarks designed to assess how well | |
| L17: different LLMs perform as agents in real-world scenarios, ... | |
| L18: * 【4†A state-of-the-art search API purpose-built for agents; publish_date: | |
| L19: none†parallel.ai】 Jul 31, 2025 — To evaluate real-world performance of the | |
| L20: Parallel Search MCP Server, we created the WISER-Search benchmark which blends | |
| L21: WISER-Fresh (queries ... | |
| L22: * 【5†AI Agent Benchmarks are Broken; publish_date: none†medium.com】 We break | |
| L23: down the failure modes in current AI agent benchmarks and introduce a checklist | |
| L24: that minimizes the gamability of AI agent benchmarks. | |
| L25: * 【6†Benchmarks and Tree Search for Multimodal LLM Web Agents; publish_date: | |
| L26: none†dpfried.github.io】 2024, When is Tree Search Useful? ○ Dealing with | |
| L27: destructive actions. ○ Some things on the web are very difficult to undo, e.g., | |
| L28: ordering an item. 56. | |
| L29: * 【7†-Bench: Benchmarking AI agents for the real-world; publish_date: | |
| L30: none†sierra.ai】 Jun 20, 2024 — τ-bench measures an agent's ability to interact | |
| L31: with (simulated) human users and programmatic APIs while following domain- | |
| L32: specific policies in a consistent ... | |
| L33: * 【8†Browser Use = state of the art Web Agent; publish_date: none†browser- | |
| L34: use.com】 Dec 15, 2024 — Browser Use has achieved state-of-the-art performance on | |
| L35: the WebVoyager benchmark, with an impressive 89.1% success rate across 586 | |
| L36: diverse web tasks. | |
| L37: * 【9†FutureSearch Benchmarks; publish_date: none†evals.futuresearch.ai】 Find | |
| L38: the original source of a given claim. Example: From , more than 8 out of 1000 | |
| L39: users clicked on a phishing link monthly in 2024, up 190% vs 2023.</result> | |
| <result>[1] 搜索智能体 基准测试 (Search_Results/搜索智能体 基准测试) | |
| **viewing lines [0 - 33] of 33** | |
| L0: | |
| L1: URL: Search_Results/搜索智能体 基准测试 | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WideSearch：揭示AI 智能体缺失的「广度」能力; publish_date: none†zhuanlan.zhihu.com】 Aug | |
| L5: 16, 2025 — 为系统评估智能体在该任务上的能力，论文构建了第一个专门的基准测试 WideSearch ，包含200 个源于真实世界、横跨18 | |
| L6: 个领域的高质量任务。 通过对超过10 个 ... | |
| L7: * 【1†GAIA: 一个严苛的智能体基准- HuggingFace; publish_date: none†www.cnblogs.com】 Jul 9, | |
| L8: 2024 — 我们使用一个用库构建的代码智能体 在GAIA 基准上进行测试，这可以说是最困难、最全面的智能体基准测试……最终我们取得了第一名的成绩！ | |
| L9: GAIA: 一个严苛的 ... | |
| L10: * 【2†AI搜索智能体遭遇新挑战：滑铁卢大学团队提出更公平透明的 ...; publish_date: none†www.techwalker.com】 | |
| L11: Aug 14, 2025 — | |
| L12: 目前评测AI搜索智能体主要依靠BrowseComp这样的基准测试，它就像一场实时的开卷考试，让AI在真实的网络环境中搜索信息来回答复杂问题。听起来很合理 ... | |
| L13: * 【3†Agentic AI基础设施实践经验系列（六）：Agent质量评估 - AWS; publish_date: | |
| L14: none†aws.amazon.com】 Sep 19, 2025 — TAU-bench | |
| L15: 是一个评估AI智能体在真实世界环境中可靠性的基准测试。它评估智能体是否能够在动态的多轮对话中与用户进行交互，理解需求并完成任务。T-bench ... | |
| L16: * 【4†DeepAgent：能自己找工具的通用推理智能体 - 高瓴人工智能学院; publish_date: none†ai.ruc.edu.cn】 | |
| L17: Nov 6, 2025 — 在八大基准测试中，DeepAgent在绝大多数任务上全面领先所有基线模型。 | |
| L18: 开放环境优势:在最具挑战的“开放工具检索”场景下(如ToolBench)，其成功率达到64%，远 ... | |
| L19: * 【5†BrowseComp：为浏览智能体设计的简单而具挑战性的基准测试; publish_date: none†blog.csdn.net】 Sep | |
| L20: 22, 2025 — 该基准测试由OpenAI团队开发，旨在推动更可信赖和可靠的AI代理研究。 核心特点. | |
| L21: 挑战性问题设计：BrowseComp的问题设计遵循严格的难度标准：. 人类创建者确保 ... | |
| L22: * 【6†什么是GAIA？; publish_date: none†huggingface.co】 什么是GAIA？ GAIA | |
| L23: 是一个用于评估AI助手在需要核心能力组合的真实世界任务上的表现的基准，这些核心能力包括推理、多模态理解、网页浏览和熟练的工具使用。 | |
| L24: * 【7†SuperCLUE-Agent: Agent智能体中文原生任务能力测评基准; publish_date: | |
| L25: none†www.cluebenchmarks.com】 AI | |
| L26: Agent是一个能够自主理解、规划决策和执行复杂任务的智能体，有代表性的Agent比如AutoGPT、AgentGPT等。 | |
| L27: 现有关于Agent能力的测评，主要是在英文场景或任务的测评。目前还 ... | |
| L28: * 【8†Gaia2 与ARE：赋能社区的智能体评测; publish_date: none†hub.baai.ac.cn】 Sep 24, 2025 — | |
| L29: 快速评估任意智能体：基于真实或模拟数据，测试不同规则、工具、内容和验证方式下的表现 · 测试智能体的 工具调用与编排能力：可结合本地应用或MCP 工具 · 生成 | |
| L30: ... | |
| L31: * 【9†许多AI 智能体评测基准并不可靠; publish_date: none†my.oschina.net】 Aug 13, 2025 — | |
| L32: 当前AI智能体基准测试存在严重缺陷，亟需更严谨的评估框架。作者提出AI智能体基准测试核查清单（ABC），包含43个项目，旨在识别基准测试潜在陷阱，构建可靠评估 | |
| L33: ...</result> | |
| </function_results> | |
| <think>第一个搜索结果（针对“search agent benchmark 2024”）显示了一些相关条目，例如“HSCodeComp: A Realistic and Expert-level Benchmark for ...”、“open-compass/GTA - A Benchmark for General Tool Agents”、“10 AI agent benchmarks”、“WISER-Search benchmark”、“Benchmarks and Tree Search for Multimodal LLM Web Agents”、“τ-bench”、“WebVoyager benchmark”、“FutureSearch Benchmarks”。这些可能都与搜索智能体相关。第二个搜索结果（针对“搜索智能体 基准测试”）显示了一些中文资料，例如“WideSearch”、“BrowseComp”、“GAIA”、“SuperCLUE-Agent”等。这些都可能与搜索智能体相关。为了全面了解这些基准测试，我们需要同时打开并分析其中几个最有潜力的结果。具体来说，我们将打开第一个搜索结果中的“HSCodeComp”（索引0）和“open-compass/GTA”（索引1），以及第二个搜索结果中的“WideSearch”（索引0）和“BrowseComp”（索引5）。</think> | |
| <｜DSML｜function_calls> | |
| <｜DSML｜invoke name="open"> | |
| <｜DSML｜parameter name="cursor" string="false">0</｜DSML｜parameter> | |
| <｜DSML｜parameter name="id" string="false">0</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="open"> | |
| <｜DSML｜parameter name="cursor" string="false">0</｜DSML｜parameter> | |
| <｜DSML｜parameter name="id" string="false">1</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="open"> | |
| <｜DSML｜parameter name="cursor" string="false">1</｜DSML｜parameter> | |
| <｜DSML｜parameter name="id" string="false">0</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="open"> | |
| <｜DSML｜parameter name="cursor" string="false">1</｜DSML｜parameter> | |
| <｜DSML｜parameter name="id" string="false">5</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| </｜DSML｜function_calls><｜end▁of▁sentence｜> | |
| <function_results> | |
| <result>[2] (https://ar5iv.org/html/2510.19631v1) | |
| **viewing lines [0 - 119] of 983** | |
| L0: | |
| L1: URL: https://ar5iv.org/html/2510.19631v1 | |
| L2: # HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in | |
| L3: Hierarchical Rule Application | |
| L4: | |
| L5: Yiqian Yang† Tian Lan† Qianghuai Jia∗ Li Zhu Hui Jiang Hang Zhu Longyue Wang | |
| L6: Weihua Luo Kaifu Zhang | |
| L7: | |
| L8: Alibaba International Digital Commerce∗* Corresponding Author: Qianghuai Jia | |
| L9: (qianghuai.jqh@alibaba-inc.com) | |
| L10: †\dagger Equal Contribution: Yiqian Yang | |
| L11: | |
| L12: Tian Lan | |
| L13: | |
| L14: ###### Abstract | |
| L15: | |
| L16: Abstract | |
| L17: | |
| L18: Effective deep search agents must not only access open-domain and domain- | |
| L19: specific knowledge but also apply complex rules—such as legal clauses, medical | |
| L20: manuals and tariff rules. These rules often feature vague boundaries and | |
| L21: implicit logic relationships, making precise application challenging for agents. | |
| L22: However, this critical capability is largely overlooked by current agent | |
| L23: benchmarks. To fill this gap, we introduce HSCodeComp, the first realistic, | |
| L24: expert-level e-commerce benchmark designed to evaluate deep search agents in | |
| L25: hierarchical rule application. In this task, the deep reasoning process of | |
| L26: agents is guided by these rules to predict 10-digit Harmonized System Code | |
| L27: (HSCode) of products with noisy but realistic descriptions. These codes, | |
| L28: established by the World Customs Organization, are vital for global supply chain | |
| L29: efficiency. Built from real-world data collected from large-scale e-commerce | |
| L30: platforms, our proposed HSCodeComp comprises 632 product entries spanning | |
| L31: diverse product categories, with these HSCodes annotated by several human | |
| L32: experts. Extensive experimental results on several state-of-the-art LLMs, open- | |
| L33: source, and closed-source agents reveal a huge performance gap: best agent | |
| L34: achieves only 46.8% 10-digit accuracy, far below human experts at 95.0%. | |
| L35: Besides, detailed analysis demonstrates the challenges of hierarchical rule | |
| L36: application, and test-time scaling fails to improve performance further. | |
| L37: | |
| L38: ## 1 Introduction | |
| L39: | |
| L40: Deep search agents have demonstrated significant value in solving complex real- | |
| L41: world problems, where robust external knowledge utilization constitutes a | |
| L42: critical capability [Wu et al., 2025, Tao et al., 2025, Li et al., 2025b]. To | |
| L43: evaluate this capability, numerous established benchmarks are proposed to assess | |
| L44: agents in utilizing open-domain data (e.g., GAIA [Mialon et al., 2023b] and | |
| L45: BrowseComp [Wei et al., 2025]) and domain-specific data (e.g., WebMall [Peeters | |
| L46: et al., 2025a], FinSearchComp [Hu et al., 2025a] and MedBrowseComp [Yu et al., | |
| L47: 2025b]). | |
| L48: | |
| L49: Beyond open-domain and domain-specific data, agents also need to effectively | |
| L50: apply rules that encode human expert knowledge, particularly in scenarios like | |
| L51: law, medical and e-commerce [Li et al., 2025a, Chen et al., 2025b, Yao et al., | |
| L52: 2022, Chollet et al., 2025]. For instance, legal case adjudication require | |
| L53: interpreting abstract legal provisions, and accurate e-commerce product | |
| L54: classification in depends on tariff rules [Grainger, 2024]. Previous works have | |
| L55: defined rule application as using specific logical rules with supporting facts | |
| L56: to derive conclusions [Wang et al., 2024, Servantez et al., 2024]. In contrast, | |
| L57: we define it as a core capability for deep search agents, where human-written | |
| L58: rules are systematically applied to guide complex reasoning and decision-making | |
| L59: [Sadowski and Chudziak, 2025]. Building on this observation, we categorize | |
| L60: knowledge data for deep search agents into three levels (Figure 1, left), with | |
| L61: increasing knowledge complexity: (1) Level 1: Open-domain Data - Tests | |
| L62: understanding and deep reasoning abilities of agents on long-form web content. | |
| L63: Established benchmarks include GAIA [Mialon et al., 2023b] and BrowseComp [Wei | |
| L64: et al., 2025]; (2) Level 2: Structured Data - Assesses agents to precisely | |
| L65: utilize structured data such as databases and knowledge graphs, as seen in | |
| L66: domain-specific benchmarks like WebMall [Peeters et al., 2025a], MedBrowseComp | |
| L67: [Chen et al., 2025b] and FinSearchComp [Hu et al., 2025a]; (3) Level 3: Rule | |
| L68: Data - Evaluates agents to apply complex and abstract rules [Chollet et al., | |
| L69: 2025]. This level presents two key challenges: (a) making accurate decisions | |
| L70: when rules contain vague natural language descriptions [Sadowski and Chudziak, | |
| L71: 2025]; and (b) reasoning about logical dependencies among rules, such as | |
| L72: exception clauses and cross-category relationships [Guha et al., 2023]. Despite | |
| L73: the importance of rule application in real-world scenarios, current agent | |
| L74: benchmarks largely overlook its evaluation. | |
| L75: | |
| L76: To fill this gap, we introduce HSCodeComp (short for the Harmonized System Code | |
| L77: (HSCode) Competition), the first realistic, expert-level e-commerce benchmark | |
| L78: designed to evaluate agents in predicting complete 10-digit Harmonized System | |
| L79: Code (HSCode) of the product, using hierarchical rules (e.g., eWTP tariff | |
| L80: rules111https://www.ewtp.com/web/smart/hscode). HSCodes organize products | |
| L81: through a hierarchical structure spanning over 5,000 distinct codes across | |
| L82: multiple classification levels, representing the global standard for classifying | |
| L83: traded international goods, established by the World Customs Organization and | |
| L84: implemented across more than 200 countries for customs clearance and tariff | |
| L85: determination [Grainger, 2024, Nath et al., 2025]. Built from the data of the | |
| L86: large-scale e-commerce platforms, our proposed HSCodeComp comprises 632 | |
| L87: carefully curated product entries, encompassing 27 unique HS chapters and 32 | |
| L88: distinct first-level categories. These HSCodes have been rigorously annotated by | |
| L89: multiple e-commerce domain experts, ensuring that HSCodeComp is expert-level. | |
| L90: Accurately predicting the exact 10-digit HSCode presents significant challenges: | |
| L91: agents must perform multi-hop hierarchical reasoning with complex tariff rules | |
| L92: while processing noisy but realistic product descriptions that often contain | |
| L93: abbreviations, language variations, or incomplete information. | |
| L94: | |
| L95: Extensive experiments on the state-of-the-art baselines, including 14 advanced | |
| L96: foundation models, 6 advanced open-source agent systems and 3 closed-source | |
| L97: agent systems, demonstrate that HSCode prediction task remains a substantial | |
| L98: challenge for current AI approaches. As shown in the Figure 1 (right), even the | |
| L99: best-performing system (SmolAgent [Roucher et al., 2025] with GPT-5) achieves | |
| L100: only 46.8% accuracy, substantially below the 95.0% accuracy attained by human | |
| L101: experts. Further detailed analysis reveals that existing agent systems lack | |
| L102: critical capabilities required for this complex hierarchical rule applications. | |
| L103: Notably, test-time scaling approach—which has proven effective in other | |
| L104: reasoning tasks [Guo et al., 2025, Liu et al., 2025]—fail to improve performance | |
| L105: on HSCodeComp. These observations demonstrate the challenging nature of our | |
| L106: proposed HSCodeComp, highlighting the need for more effective designs of agent | |
| L107: systems. To facilitate future research, we will publicly release codes and the | |
| L108: benchmark dataset of HSCodeComp. | |
| L109: | |
| L110: ## 2 Related Works | |
| L111: | |
| L112: ### 2.1 Previous Works in HSCode Prediction | |
| L113: | |
| L114: Previous works treat HSCode prediction as the e-commerce text classification | |
| L115: task [Grainger, 2024], using pre-trained BERT models [Liao et al., 2024, Shubham | |
| L116: et al., 2022] or Large Language Models (LLMs) prompting [Hussain and Ahmed, | |
| L117: 2023]. However, these approaches fail to leverage domain-specific knowledge, | |
| L118: especially the rules written by human experts [Hussain and Ahmed, 2023, Judy, | |
| L119: 2024]. Besides, existing HSCode benchmarks face two critical limitations [Judy, </result> | |
| <result>Error fetching URL `https://github.com/open-compass/GTA`</result> | |
| <result>Error fetching URL `https://zhuanlan.zhihu.com/p/1939316761775301093`</result> | |
| <result>[3] (https://blog.csdn.net/qq_41472205/article/details/151974603) | |
| **viewing lines [0 - 167] of 187** | |
| L0: | |
| L1: URL: https://blog.csdn.net/qq_41472205/article/details/151974603 | |
| L2: # BrowseComp：为浏览智能体设计的简单而具挑战性的基准测试 | |
| L3: | |
| L4: BrowseComp：AI浏览能力评估基准 | |
| L5: | |
| L6: 最新推荐文章于 2025-11-12 13:40:20 发布 | |
| L7: | |
| L8: 原创 于 2025-09-22 22:33:04 发布 · 1.3k 阅读 | |
| L9: | |
| L10: · 9 | |
| L11: · 25 · | |
| L12: CC 4.0 BY-SA版权 | |
| L13: | |
| L14: 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 | |
| L15: | |
| L16: ## BrowseComp：为浏览智能体设计的简单而具挑战性的基准测试 | |
| L17: | |
| L18: 在人工智能从基础聊天机器人向推理器和智能体发展的进程中，具备浏览互联网能力的人工智能模型正变得越来越重要。今天，我们将介绍一个名为BrowseComp的创新基准 | |
| L19: 测试，它专门设计用于评估AI代理在复杂网络浏览任务中的能力。 | |
| L20: | |
| L21: ### 什么是BrowseComp？ | |
| L22: | |
| L23: BrowseComp（全称Browsing Competition）是一个包含1,266个挑战性问题的基准测试集，专门用于衡量AI代理在互联网上持续导航、寻找难 | |
| L24: 以找到的纠缠信息的能力。该基准测试由OpenAI团队开发，旨在推动更可信赖和可靠的AI代理研究。 | |
| L25: | |
| L26: #### 核心特点 | |
| L27: | |
| L28: 挑战性问题设计：BrowseComp的问题设计遵循严格的难度标准： | |
| L29: | |
| L30: - 人类创建者确保问题在10分钟内无法被人解决 | |
| L31: - 现有模型（包括带浏览功能的ChatGPT和早期版本的OpenAI Deep Research）无法解决 | |
| L32: - 通过5次简单Google搜索无法在结果首页找到答案 | |
| L33: | |
| L34: 简单易验证：尽管问题极具挑战性，但答案形式简单——都是短字符串，便于自动验证模型输出的正确性。 | |
| L35: | |
| L36: ### 为什么需要BrowseComp？ | |
| L37: | |
| L38: #### 现有基准的局限性 | |
| L39: | |
| L40: 传统的信息检索基准（如TriviaQA、HotpotQA等）主要关注易于查找的信息，随着语言模型的进步，这些基准已经趋于饱和。而BrowseComp专注于那些需 | |
| L41: 要浏览大量网站才能解决的"硬核"问题。 | |
| L42: | |
| L43: #### 模拟真实挑战 | |
| L44: | |
| L45: BrowseComp问题通常采用"逆向设计"方法：创建者从一个已知事实出发，构建一个搜索空间巨大但验证简单的问题。例如： | |
| L46: | |
| L47: “找出2018-2023年间在EMNLP会议上发表、第一作者本科毕业于达特茅斯学院、第四作者本科毕业于宾夕法尼亚大学的科学论文标题” | |
| L48: | |
| L49: 这类问题验证简单，但解决起来需要检查数千篇论文并调查每位作者的背景。 | |
| L50: | |
| L51: ### 数据集特点 | |
| L52: | |
| L53: #### 主题多样性 | |
| L54: | |
| L55: BrowseComp涵盖了广泛的主题领域（如图2所示），包括历史、科学、文化等。创建者被鼓励基于个人兴趣设计问题，这有助于提高数据质量和参与度。 | |
| L56: | |
| L57: #### 质量保证 | |
| L58: | |
| L59: 为确保答案的唯一性，创建者需要： | |
| L60: | |
| L61: - 对问题内容有足够了解，确信没有其他有效答案 | |
| L62: - 如果不确定，则添加更多约束条件 | |
| L63: - 接受其他创建者的验证反馈 | |
| L64: | |
| L65: ### 人类表现基准 | |
| L66: | |
| L67: 为了衡量BrowseComp的难度，研究人员让人类创建者尝试解决问题（不能解答自己创建的问题）。结果显示： | |
| L68: | |
| L69: - **70.8%**的问题在2小时搜索后人类选择放弃 | |
| L70: - **29.2%**的问题被成功解决 | |
| L71: - 在解决的问题中，**86.4%**的人类答案与参考答案一致 | |
| L72: | |
| L73: 这表明BrowseComp确实极具挑战性，即使是熟悉数据集的人类专家也难以在有限时间内解决大部分问题。 | |
| L74: | |
| L75: ### AI模型表现评估 | |
| L76: | |
| L77: #### 各模型对比 | |
| L78: | |
| L79: 研究人员评估了多种模型在BrowseComp上的表现： | |
| L80: | |
| L81: 模型 | 准确率(%) | 校准误差(%) | |
| L82: ---|---|--- | |
| L83: GPT-4o | 0.6 | 69 | |
| L84: GPT-4o（带浏览） | 1.9 | 82 | |
| L85: GPT-4.5 | 0.9 | 68 | |
| L86: OpenAI o1 | 9.9 | 65 | |
| L87: Deep Research | 51.5 | 91 | |
| L88: | |
| L89: #### 关键发现 | |
| L90: | |
| L91: - 基础模型表现不佳：GPT-4o和GPT-4.5准确率接近零，凸显了基准的难度 | |
| L92: - 浏览功能带来有限提升：启用浏览功能的GPT-4o准确率略有提高，但仍很低 | |
| L93: - 推理能力的重要性：OpenAI o1虽然没有浏览能力，但凭借更强的推理能力获得较高准确率 | |
| L94: - 专业模型的优势：专门为持久网络浏览训练的Deep Research模型解决了约一半的问题 | |
| L95: | |
| L96: #### 计算资源与性能关系 | |
| L97: | |
| L98: 研究表明，BrowseComp性能随测试时计算资源的增加而平滑提升（如图1所示）。这与智能体模型的特性一致——更多计算资源允许模型浏览更多网站，从而提高找到正确 | |
| L99: 答案的机会。 | |
| L100: | |
| L101: ### 进阶策略分析 | |
| L102: | |
| L103: #### 聚合策略的效果 | |
| L104: | |
| L105: 通过让模型多次尝试同一问题并采用投票策略，可以显著提升性能： | |
| L106: | |
| L107: - 多数投票：选择样本中最常见的答案 | |
| L108: - 加权投票：根据模型置信度加权投票 | |
| L109: - 最佳选择：选择置信度最高的答案 | |
| L110: | |
| L111: 这些方法将Deep Research的性能提升了15-25%，表明模型通常能够识别自己的正确答案。 | |
| L112: | |
| L113: #### 任务难度分布 | |
| L114: | |
| L115: 分析显示，BrowseComp中的任务难度分布广泛： | |
| L116: | |
| L117: - 16%的任务被Deep Research完美解决（100%通过率） | |
| L118: - 14%的任务完全失败（0%通过率） | |
| L119: - 其余任务处于中间难度水平 | |
| L120: | |
| L121: ### BrowseComp的意义与局限性 | |
| L122: | |
| L123: #### 作为评估工具的价值 | |
| L124: | |
| L125: BrowseComp可被视为浏览智能体的"编程竞赛"——虽然不全面，但对核心浏览能力提供了有用的衡量。正如在编程竞赛中表现出色的模型很可能在其他编码任务中表现良 | |
| L126: 好，能够解决BrowseComp的模型在定位难以查找信息方面应该具有很强的能力。 | |
| L127: | |
| L128: #### 已知局限性 | |
| L129: | |
| L130: - 不涉及真实用户查询分布：如生成长回答或解决查询歧义等挑战 | |
| L131: - 答案唯一性假设：虽然尽力确保，但不能完全保证没有其他有效答案 | |
| L132: - 单模态限制：当前仅关注文本信息检索，未来可扩展至图像、视频等模态 | |
| L133: | |
| L134: ### 结论与展望 | |
| L135: | |
| L136: BrowseComp为评估AI代理的网络浏览能力提供了一个具有挑战性且易于使用的基准。它强调的持久性、创造性和战略性浏览能力，正是未来AI智能体在实际应用中所需 | |
| L137: 的核心技能。 | |
| L138: | |
| L139: 随着更多研究团队在BrowseComp上测试他们的模型，我们期待看到浏览智能体技术的快速进步，最终使AI能够更可靠地帮助我们获取那些深藏在互联网角落的宝贵信息。 | |
| L140: | |
| L141: 访问地址：BrowseComp已在GitHub上开源，地址为 https://github.com/openai/simple-evals | |
| L142: | |
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| L157: 需支付：10.00元 | |
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| L167: 扫码支付</result> | |
| </function_results> | |
| <think>打开的HSCodeComp页面详细介绍了这个基准测试，它是一个用于评估深度搜索智能体在分层规则应用方面能力的基准测试，特点包括：基于真实世界电商数据、任务复杂、需要多跳层次推理、使用嘈杂的产品描述，并且标注由专家完成。使用场景是评估智能体在规则应用方面的能力，例如关税代码分类。例题是预测产品的10位HS编码。打开的BrowseComp页面详细介绍了这个基准测试，它是一个用于评估浏览智能体（即能够在互联网上导航、搜索信息的智能体）的基准测试。其特点包括：包含1266个具有挑战性的问题，需要持续导航和搜索难以查找的信息；答案形式简单，便于自动验证；问题设计确保人类在10分钟内无法解决，且现有模型也无法解决；涵盖了广泛的主题领域。使用场景是评估智能体在复杂网络浏览任务中的能力。例题是：“找出2018-2023年间在EMNLP会议上发表、第一作者本科毕业于达特茅斯学院、第四作者本科毕业于宾夕法尼亚大学的科学论文标题”。另外两个打开的页面（open-compass/GTA和WideSearch）出现了错误，可能链接已失效或需要其他访问方式。为了进一步获取其他相关基准测试的信息，我们需要同时进行多项搜索。考虑到用户可能对搜索智能体基准测试有广泛需求，我们将同时搜索“WebVoyager benchmark”、“WebArena benchmark”、“GAIA benchmark”、“ToolBench benchmark”、“HotpotQA benchmark”、“FEVER benchmark”、“TriviaQA benchmark”、“Natural Questions benchmark”、“MS MARCO benchmark”、“BEIR benchmark”、“MIRACL benchmark”、“Zero-shot IR benchmark”、“WebGPT benchmark”、“WebShop benchmark”、“ALFWorld benchmark”、“Mind2Web benchmark”、“VisualWebArena benchmark”、“SearchBench benchmark”、“WebVLN benchmark”、“WebNav benchmark”。这些搜索将帮助我们全面了解现有基准测试的概况。</think> | |
| <｜DSML｜function_calls> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">WebVoyager benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">WebArena benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">GAIA benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">ToolBench benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">HotpotQA benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">FEVER benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">TriviaQA benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">Natural Questions benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">MS MARCO benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">BEIR benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">MIRACL benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">Zero-shot IR benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">WebGPT benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">WebShop benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">ALFWorld benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">Mind2Web benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">VisualWebArena benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">SearchBench benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">WebVLN benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| <｜DSML｜invoke name="search"> | |
| <｜DSML｜parameter name="query" string="true">WebNav benchmark</｜DSML｜parameter> | |
| <｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter> | |
| <｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter> | |
| </｜DSML｜invoke> | |
| </｜DSML｜function_calls><｜end▁of▁sentence｜> | |
| <function_results> | |
| <result>[4] WebVoyager benchmark (Search_Results/WebVoyager benchmark) | |
| **viewing lines [0 - 40] of 40** | |
| L0: | |
| L1: URL: Search_Results/WebVoyager benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WebVoyager Benchmark; publish_date: none†www.trykura.com】 Performance | |
| L5: measured across various websites in WebVoyager shows Kura consistently | |
| L6: outperforming other agents, with particularly strong results on e-commerce ... | |
| L7: * 【1†WebVoyager: Building an End-to-End Web Agent with ...; publish_date: | |
| L8: none†github.com】 WebVoyager is an innovative Large Multimodal Model (LMM) | |
| L9: powered web agent that can complete user instructions end-to-end by interacting | |
| L10: with real-world ... | |
| L11: * 【2†AI Browser Agent Leaderboard | Steel.dev; publish_date: | |
| L12: none†leaderboard.steel.dev】 See how various AI browser agents stack up based on | |
| L13: their accuracy in completing web-based tasks on the WebVoyager benchmark. | |
| L14: * 【3†[2401.13919] WebVoyager: Building an End-to-End Web ...; publish_date: | |
| L15: none†arxiv.org】 by H He · 2024 · Cited by 282 — We show that WebVoyager achieves | |
| L16: a 59.1% task success rate on our benchmark, significantly surpassing the | |
| L17: performance of both GPT-4 (All ... | |
| L18: * 【4†Our Agent-E SOTA Results on the WebVoyager Benchmark; publish_date: | |
| L19: none†www.emergence.ai】 Jul 11, 2024 — WebVoyager is a benchmark that tests an | |
| L20: agent's capabilities for navigation on dynamic live websites. It is more | |
| L21: representative than WebArena [4] ... | |
| L22: * 【5†Browser Use = state of the art Web Agent; publish_date: none†browser- | |
| L23: use.com】 Dec 15, 2024 — Browser Use has achieved state-of-the-art performance on | |
| L24: the WebVoyager benchmark, with an impressive 89.1% success rate across 586 | |
| L25: diverse web tasks. | |
| L26: * 【6†Magnitude achieves SOTA 94% on WebVoyager benchmark; publish_date: | |
| L27: none†github.com】 Magnitude achieves state-of-the-art performance with 93.9% | |
| L28: success rate on WebVoyager, beating all other browser agents. | |
| L29: * 【7†WebVoyager: Autonomous Web Agent Benchmark; publish_date: | |
| L30: none†www.emergentmind.com】 3 days ago — WebVoyager Benchmark is a comprehensive | |
| L31: evaluation suite for autonomous web agents, featuring 643 tasks across 15 | |
| L32: popular websites. | |
| L33: * 【8†WebVoyager Benchmark Results; publish_date: none†www.browserable.ai】 | |
| L34: Browserable has achieved 90.4% on the WebVoyager benchmark. This is best-in- | |
| L35: class performance across all web agents. This was done across 567 web tasks | |
| L36: which ... | |
| L37: * 【9†89% achieved on WebVoyager using Anchor + Browser Use; publish_date: | |
| L38: none†www.reddit.com】 Thanks to the amazing work from the browser-use open-source | |
| L39: community and the built-in support from Anchor Browser, we've hit an 89% score | |
| L40: on WebVoyager.</result> | |
| <result>[5] WebArena benchmark (Search_Results/WebArena benchmark) | |
| **viewing lines [0 - 42] of 42** | |
| L0: | |
| L1: URL: Search_Results/WebArena benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WebArena: A Realistic Web Environment for Building ...; publish_date: | |
| L5: none†webarena.dev】 Our benchmark is implemented in our fully interactable | |
| L6: highly-realistic WebArena environment. It features diverse tasks human may | |
| L7: encounter in their daily ... | |
| L8: * 【1†[2307.13854] WebArena: A Realistic Web Environment for ...; publish_date: | |
| L9: none†arxiv.org】 by S Zhou · 2023 · Cited by 637 — Building upon our | |
| L10: environment, we release a set of benchmark tasks focusing on evaluating the | |
| L11: functional correctness of task completions. | |
| L12: * 【2†WebArena: A Realistic Web Environment for Building ...; publish_date: | |
| L13: none†www.cmu.edu】 WebArena introduces a benchmark on interpreting high-level | |
| L14: realistic natural language command to concrete web-based interactions. We | |
| L15: provide annotated programs ... | |
| L16: * 【3†GitHub - web-arena-x/webarena: Code repo for ...; publish_date: | |
| L17: none†github.com】 [12/20/2024] Check out our new benchmark on even more | |
| L18: consequential tasks, including terminal use and coding, TheAgentCompany. | |
| L19: [12/21/2023] We release the ... | |
| L20: * 【4†WebArena Benchmark and the State of Agentic AI; publish_date: | |
| L21: none†medium.com】 In short, WebArena established a new standard for realism and | |
| L22: complexity in web agent evaluation, forcing AI agents to operate in dynamic, | |
| L23: high- ... | |
| L24: * 【5†WebArena: A Realistic Web Environment for Building ...; publish_date: | |
| L25: none†huggingface.co】 Jul 25, 2023 — WebArena, a realistic and reproducible | |
| L26: environment, evaluates the performance of autonomous agents performing complex | |
| L27: tasks on websites using ... | |
| L28: * 【6†WebArena Benchmark: Evaluating Web Agents; publish_date: | |
| L29: none†www.emergentmind.com】 Jun 30, 2025 — WebArena Benchmark is a self-contained | |
| L30: suite that evaluates autonomous agents on realistic, multi-step web tasks using | |
| L31: natural language ... | |
| L32: * 【7†VisualWebArena is a benchmark for multimodal agents.; publish_date: | |
| L33: none†github.com】 VisualWebArena is a realistic and diverse benchmark for | |
| L34: evaluating multimodal autonomous language agents. It comprises of a set of | |
| L35: diverse and complex web-based ... | |
| L36: * 【8†WebDev Arena Leaderboard - LMArena; publish_date: none†web.lmarena.ai】 | |
| L37: WebDev Arena is a real-time AI coding competition where models go head-to-head | |
| L38: in web development challenges, developed by LMArena. | |
| L39: * 【9†WebArena: A Realistic Web Environment for Building ...; publish_date: | |
| L40: none†arxiv.org】 Apr 16, 2024 — We use this benchmark to evaluate several agents | |
| L41: that can follow NL command and perform web-based tasks (§4). These agents are | |
| L42: implemented in a ...</result> | |
| <result>[6] GAIA benchmark (Search_Results/GAIA benchmark) | |
| **viewing lines [0 - 41] of 41** | |
| L0: | |
| L1: URL: Search_Results/GAIA benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†GAIA Leaderboard - a Hugging Face Space by ...; publish_date: | |
| L5: none†huggingface.co】 GAIA is a benchmark which aims at evaluating next- | |
| L6: generation LLMs (LLMs with augmented capabilities due to added tooling, | |
| L7: efficient prompting, access to search ... | |
| L8: * 【1†[2311.12983] GAIA: a benchmark for General AI Assistants; publish_date: | |
| L9: none†arxiv.org】 by G Mialon · 2023 · Cited by 367 — GAIA proposes real-world | |
| L10: questions that require a set of fundamental abilities such as reasoning, multi- | |
| L11: modality handling, web browsing, and generally tool-use ... | |
| L12: * 【2†GAIA benchmark; publish_date: none†huggingface.co】 This is the | |
| L13: organisation page for all things related to GAIA, a benchmark for General AI | |
| L14: Assistants. You can find all the information and links on the GAIA ... | |
| L15: * 【3†GAIA: A Benchmark for General AI Assistants; publish_date: | |
| L16: none†ukgovernmentbeis.github.io】 This is an Inspect AI implementation of the | |
| L17: GAIA (General AI Assistants) benchmark, consisting of 450 questions testing tool | |
| L18: use on realistic assistant tasks. | |
| L19: * 【4†GAIA: a benchmark for general AI assistants | Research; publish_date: | |
| L20: none†ai.meta.com】 May 6, 2024 — GAIA proposes real-world questions that require | |
| L21: a set of fundamental abilities such as reasoning, multi-modality handling, web | |
| L22: browsing, and generally tool-use ... | |
| L23: * 【5†HAL: GAIA Leaderboard; publish_date: none†hal.cs.princeton.edu】 GAIA is a | |
| L24: benchmark for General AI Assistants that requires a set of fundamental | |
| L25: abilities such as reasoning, multi-modality handling, web browsing, and tool- | |
| L26: ... | |
| L27: * 【6†GAIA: The LLM Agent Benchmark Everyone's Talking About; publish_date: | |
| L28: none†towardsdatascience.com】 May 29, 2025 — GAIA stands for General AI | |
| L29: Assistants benchmark [1]. This benchmark was introduced to specifically evaluate | |
| L30: LLM agents on their ability to act as general- ... | |
| L31: * 【7†GAIA: a benchmark for General AI Assistants; publish_date: | |
| L32: none†openreview.net】 by G Mialon · Cited by 367 — GAIA proposes real-world | |
| L33: questions that require a set of fundamental abilities such as reasoning, multi- | |
| L34: modality handling, web browsing, and generally tool-use ... | |
| L35: * 【8†Rethinking AI Evaluation: Introducing the GAIA Benchmark; publish_date: | |
| L36: none†medium.com】 The authors introduce GAIA, a benchmark designed to assess the | |
| L37: robustness of AI systems across a variety of practical tasks. | |
| L38: * 【9†H2O.ai Tops the General AI Assistant (GAIA) Test; publish_date: | |
| L39: none†h2o.ai】 Mar 17, 2025 — Our h2oGPTe Agent has once again claimed the #1 spot | |
| L40: on the prestigious GAIA (General AI Assistants) benchmark with an impressive | |
| L41: 75% accuracy rate.</result> | |
| <result>[7] ToolBench benchmark (Search_Results/ToolBench benchmark) | |
| **viewing lines [0 - 40] of 40** | |
| L0: | |
| L1: URL: Search_Results/ToolBench benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†ToolBench, an evaluation suite for LLM tool manipulation ...; | |
| L5: publish_date: none†github.com】 The ToolBench is a benchmark consisting of | |
| L6: diverse software tools for real-world tasks. We also provide easy-to-use | |
| L7: infrastructure in this repository. | |
| L8: * 【1†OpenBMB/ToolBench; publish_date: none†github.com】 [2023/7/27] New version | |
| L9: ToolBench is released. ✨Here is an overview of the dataset construction, | |
| L10: training, and evaluation. ✨✨Features:. | |
| L11: * 【2†Towards Stable Large-Scale Benchmarking on Tool ...; publish_date: | |
| L12: none†arxiv.org】 by Z Guo · 2024 · Cited by 100 — We introduce StableToolBench, a | |
| L13: benchmark evolving from ToolBench, proposing a virtual API server and stable | |
| L14: evaluation system. | |
| L15: * 【3†StableToolBench - Zhicheng Guo; publish_date: none†zhichengg.github.io】 | |
| L16: We introduce StableToolBench, a benchmark evolving from ToolBench, proposing a | |
| L17: virtual API server and stable evaluation system. | |
| L18: * 【4†ToolBench | EvalScope - Read the Docs; publish_date: | |
| L19: none†evalscope.readthedocs.io】 We evaluate the effectiveness of the ToolBench | |
| L20: benchmark: ToolBench (Qin et al., 2023b). The task involves integrating API | |
| L21: calls to complete tasks. | |
| L22: * 【5†Towards Stable Large-Scale Benchmarking on Tool ...; publish_date: | |
| L23: none†aclanthology.org】 by Z Guo · 2024 · Cited by 100 — We introduce | |
| L24: StableToolBench, a benchmark evolving from ToolBench, proposing a virtual API | |
| L25: server and stable evaluation system. | |
| L26: * 【6†ML-Tool-Bench: Tool-Augmented Planning for ML Tasks; publish_date: | |
| L27: none†openreview.net】 Sep 18, 2025 — In this work, we introduce a comprehensive | |
| L28: benchmark for evaluating tool-augmented ML agents using a curated set of 61 | |
| L29: specialized tools and 15 ... | |
| L30: * 【7†-Bench: Benchmarking AI agents for the real-world; publish_date: | |
| L31: none†sierra.ai】 Jun 20, 2024 — τ-bench measures an agent's ability to interact | |
| L32: with (simulated) human users and programmatic APIs while following domain- | |
| L33: specific policies in a consistent ... | |
| L34: * 【8†ToolEval Leaderboard; publish_date: none†openbmb.github.io】 ToolEval is | |
| L35: an automatic evaluator build for tool learning which incorporates two evaluation | |
| L36: metrics, Pass Rate and Win Rate(Preference). | |
| L37: * 【9†What is the best benchmark dataset for multi-step tool-use?; | |
| L38: publish_date: none†www.reddit.com】 I'm a newbie trying to evaluate the | |
| L39: performance of different prompts strategies for multi-step tool-using, wondering | |
| L40: what is the recommended benchmark dataset ...</result> | |
| <result>[8] HotpotQA benchmark (Search_Results/HotpotQA benchmark) | |
| **viewing lines [0 - 39] of 39** | |
| L0: | |
| L1: URL: Search_Results/HotpotQA benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†HotpotQA Homepage; publish_date: none†hotpotqa.github.io】 HotpotQA is a | |
| L5: question answering dataset featuring natural, multi-hop questions, with strong | |
| L6: supervision for supporting facts to enable more explainable ...See more | |
| L7: * 【1†HotpotQA: A Dataset for Diverse, Explainable Multi-hop ...; publish_date: | |
| L8: none†arxiv.org】 by Z Yang · 2018 · Cited by 3834 — HotpotQA is a dataset with | |
| L9: 113k Wikipedia-based question-answer pairs requiring multi-document reasoning, | |
| L10: diverse questions, sentence-level ... | |
| L11: * 【2†hotpotqa/hotpot_qa · Datasets at Hugging Face; publish_date: | |
| L12: none†huggingface.co】 HotpotQA is a new dataset with 113k Wikipedia-based | |
| L13: question-answer pairs with four key features: (1) the questions require finding | |
| L14: and reasoning over multiple ...See more | |
| L15: * 【3†Why You Should Stop Using HotpotQA for AI Agents ...; publish_date: | |
| L16: none†qipeng.me】 Jul 1, 2025 — HotpotQA pioneered a class of AI tasks that | |
| L17: requires the AI system to autonomously perform multiple steps of reasoning in an | |
| L18: open-domain setting.See more | |
| L19: * 【4†hotpotqa/hotpot; publish_date: none†github.com】 A dataset for diverse, | |
| L20: explainable multi-hop question answering. This repository contains the baseline | |
| L21: model code, as well as the entire pipeline of running ...See more | |
| L22: * 【5†HotpotQA: Multi-Hop QA Benchmark; publish_date: | |
| L23: none†www.emergentmind.com】 Sep 10, 2025 — HotpotQA is a large-scale multi-hop | |
| L24: question answering benchmark featuring 112,779 Wikipedia-based Q&A pairs with | |
| L25: detailed, sentence-level ...See more | |
| L26: * 【6†HotpotQA Dataset | Papers With Code; publish_date: | |
| L27: none†paperswithcode.com】 HotpotQA is a question answering dataset collected on | |
| L28: the English Wikipedia, containing about 113K crowd-sourced questions.See more | |
| L29: * 【7†HotpotQA: A Dataset for Diverse, Explainable Multi-hop ...; publish_date: | |
| L30: none†aclanthology.org】 by Z Yang · 2018 · Cited by 3834 — HotpotQA is a dataset | |
| L31: with 113k Wikipedia-based question-answer pairs requiring multi-document | |
| L32: reasoning, diverse questions, sentence-level facts, and factoid ... | |
| L33: * 【8†Benchmark BM25S: HotpotQA; publish_date: none†www.kaggle.com】 Explore and | |
| L34: run machine learning code with Kaggle Notebooks | Using data from No attached | |
| L35: data sources. | |
| L36: * 【9†mteb/hotpotqa · Datasets at Hugging Face; publish_date: | |
| L37: none†huggingface.co】 HotpotQA is a question answering dataset featuring natural, | |
| L38: multi-hop questions, with strong supervision for supporting facts to enable | |
| L39: more explainable ...See more</result> | |
| <result>[9] FEVER benchmark (Search_Results/FEVER benchmark) | |
| **viewing lines [0 - 40] of 40** | |
| L0: | |
| L1: URL: Search_Results/FEVER benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†Fever.ai; publish_date: none†fever.ai】 We are pleased to announce that | |
| L5: FEVER9 will be co-located with EACL 2026. In this year's workshop, we will | |
| L6: introduce a new shared task focused on automated fact ... | |
| L7: * 【1†a Large-scale Dataset for Fact Extraction and VERification; publish_date: | |
| L8: none†aclanthology.org】 by J Thorne · 2018 · Cited by 2315 — In this paper we | |
| L9: introduce a new publicly available dataset for verification against textual | |
| L10: sources, FEVER: Fact Extraction. | |
| L11: * 【2†awslabs/fever: FEVER (Fact Extraction and VERification) ...; | |
| L12: publish_date: none†github.com】 In this paper we introduce a new publicly | |
| L13: available dataset for verification against textual sources, FEVER: Fact | |
| L14: Extraction and VERification. | |
| L15: * 【3†FEVER: Fact Extraction and VERification; publish_date: | |
| L16: none†www.amazon.science】 The best accuracy we achieve on labeling a claim | |
| L17: accompanied by the correct evidence is 31.87%, while if we ignore the evidence | |
| L18: we achieve 50.91%. Thus we ... | |
| L19: * 【4†FEVER Dataset; publish_date: none†fever.ai】 FEVER (Fact Extraction and | |
| L20: VERification) consists of 185,445 claims generated by altering sentences | |
| L21: extracted from Wikipedia and subsequently verified ... | |
| L22: * 【5†mteb/fever · Datasets at Hugging Face; publish_date: none†huggingface.co】 | |
| L23: FEVER. An MTEB dataset. Massive Text Embedding Benchmark. FEVER (Fact | |
| L24: Extraction and VERification) consists of 185,445 claims generated by altering | |
| L25: sentences ... | |
| L26: * 【6†FEVEROUS: Fact Extraction and VERification Over ...; publish_date: | |
| L27: none†datasets-benchmarks-proceedings.neurips.cc】 by R Aly · Cited by 359 — In | |
| L28: this paper we introduce a novel dataset and benchmark, Fact Extraction and | |
| L29: VERification Over. Unstructured and Structured information (FEVEROUS), which ... | |
| L30: * 【7†a large-scale dataset for Fact Extraction and VERification; publish_date: | |
| L31: none†arxiv.org】 by J Thorne · 2018 · Cited by 2315 — In this paper we introduce | |
| L32: a new publicly available dataset for verification against textual sources, | |
| L33: FEVER: Fact Extraction and VERification. | |
| L34: * 【8†FEVER Resources; publish_date: none†fever.ai】 2018 FEVER: a large-scale | |
| L35: dataset for Fact Extraction and VERification .bib James Thorne, Andreas Vlachos, | |
| L36: Christos Christodoulopoulos, Arpit Mittal | |
| L37: * 【9†a Large-scale Dataset for Fact Extraction and VERification; publish_date: | |
| L38: none†www.semanticscholar.org】 This paper introduces a new publicly available | |
| L39: dataset for verification against textual sources, FEVER, which consists of | |
| L40: 185,445 claims generated by ...</result> | |
| <result>[10] TriviaQA benchmark (Search_Results/TriviaQA benchmark) | |
| **viewing lines [0 - 35] of 35** | |
| L0: | |
| L1: URL: Search_Results/TriviaQA benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†TriviaQA; publish_date: none†nlp.cs.washington.edu】 TriviaQA is a reading | |
| L5: comprehension dataset containing over 650K question-answer-evidence triples. | |
| L6: TriviaQA includes 95K question-answer pairs authored ... | |
| L7: * 【1†TriviaQA: A Large Scale Distantly Supervised Challenge ...; publish_date: | |
| L8: none†aclanthology.org】 by M Joshi · 2017 · Cited by 3451 — We present TriviaQA, | |
| L9: a challenging reading comprehension dataset containing over 650K question- | |
| L10: answer-evidence triples. TriviaQA includes 95K question ... | |
| L11: * 【2†mandarjoshi/trivia_qa · Datasets at Hugging Face; publish_date: | |
| L12: none†huggingface.co】 TriviaqQA is a reading comprehension dataset containing | |
| L13: over 650K question-answer-evidence triples. TriviaqQA includes 95K question- | |
| L14: answer pairs authored by ... | |
| L15: * 【3†[1705.03551] TriviaQA: A Large Scale Distantly Supervised ...; | |
| L16: publish_date: none†arxiv.org】 by M Joshi · 2017 · Cited by 3451 — We present | |
| L17: TriviaQA, a challenging reading comprehension dataset containing over 650K | |
| L18: question-answer-evidence triples. | |
| L19: * 【4†TriviaQA; publish_date: none†epoch.ai】 An open-domain question answering | |
| L20: benchmark with challenging trivia questions paired with evidence documents. | |
| L21: * 【5†TriviaQA Leaderboard; publish_date: none†llm-stats.com】 What is the | |
| L22: TriviaQA benchmark? A large-scale reading comprehension dataset containing over | |
| L23: 650K question-answer-evidence triples. TriviaQA includes 95K ... | |
| L24: * 【6†Code for the TriviaQA reading comprehension dataset; publish_date: | |
| L25: none†github.com】 A large scale distantly supervised challenge dataset for | |
| L26: reading comprehension. In Association for Computational Linguistics (ACL) 2017, | |
| L27: Vancouver, Canada. | |
| L28: * 【7†TriviaQA - Model Benchmarks - The Regularizer; publish_date: | |
| L29: none†www.theregularizer.com】 May 4, 2025 — Compare the performance of different | |
| L30: AI models across standardized benchmarks. Higher scores generally indicate | |
| L31: better performance, but context ... | |
| L32: * 【8†TriviaQA: A Large Scale Distantly Supervised Challenge ...; publish_date: | |
| L33: none†www.cs.utexas.edu】 by M Joshi · Cited by 3445 — We present TriviaQA, a | |
| L34: challenging reading comprehension dataset contain- ing over 650K question- | |
| L35: answer-evidence triples. TriviaQA includes 95K question-.</result> | |
| <result>[11] Natural Questions benchmark (Search_Results/Natural Questions benchmark) | |
| **viewing lines [0 - 39] of 39** | |
| L0: | |
| L1: URL: Search_Results/Natural Questions benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†Natural Questions: a Benchmark for Question Answering ...; publish_date: | |
| L5: none†research.google】 by T Kwiatkowski · Cited by 4339 — We present the Natural | |
| L6: Questions corpus, a question answering dataset. Questions consist of real | |
| L7: anonymized, aggregated queries issued to the Google search ... | |
| L8: * 【1†Natural Questions: A Benchmark for Question Answering ...; publish_date: | |
| L9: none†aclanthology.org】 by T Kwiatkowski · Cited by 4308 — Abstract. We present | |
| L10: the Natural Questions corpus, a question answering data set. Questions consist | |
| L11: of real anonymized, aggregated queries issued. | |
| L12: * 【2†Google's Natural Questions; publish_date: none†ai.google.com】 Natural | |
| L13: Questions. A Benchmark for Question Answering Research. View examples · Download | |
| L14: dataset. Open Domain Question Answering. A core goal in artificial ... | |
| L15: * 【3†google-research-datasets/natural-questions; publish_date: | |
| L16: none†github.com】 Natural Questions (NQ) contains real user questions issued to | |
| L17: Google search, and answers found from Wikipedia by annotators. NQ is designed | |
| L18: for the training and ... | |
| L19: * 【4†Natural Questions: A Benchmark for Question Answering ...; publish_date: | |
| L20: none†direct.mit.edu】 Aug 1, 2019 — We present the Natural Questions corpus, a | |
| L21: question answering data set. Questions consist of real anonymized, aggregated | |
| L22: queries issued to the Google search ... | |
| L23: * 【5†ir_datasets : Natural Questions; publish_date: none†ir-datasets.com】 | |
| L24: Google Natural Questions is a Q&A dataset containing long, short, and Yes/No | |
| L25: answers from Wikipedia. ir_datasets frames this around an ad-hoc ranking setting | |
| L26: ... | |
| L27: * 【6†sentence-transformers/natural-questions · Datasets at ...; publish_date: | |
| L28: none†huggingface.co】 This dataset is a collection of question-answer pairs from | |
| L29: the Natural Questions dataset. See Natural Questions for additional information. | |
| L30: * 【7†Google's Natural Questions; publish_date: none†ai.google.com】 Natural | |
| L31: Questions contains 307K training examples, 8K examples for development, and a | |
| L32: further 8K examples for testing. In the paper, we demonstrate a human ... | |
| L33: * 【8†A Benchmark for Question Answering Research; publish_date: | |
| L34: none†www.researchgate.net】 Jul 27, 2025 — We present the Natural Questions | |
| L35: corpus, a question answering data set. Questions consist of real anonymized, | |
| L36: aggregated queries issued to the Google search ... | |
| L37: * 【9†natural-questions; publish_date: none†docs.unity.rc.umass.edu】 Sep 4, | |
| L38: 2025 — “Natural questions: a benchmark for question answering research.” | |
| L39: Transactions of the Association for Computational Linguistics 7 (2019): ...</result> | |
| <result>[12] MS MARCO benchmark (Search_Results/MS MARCO benchmark) | |
| **viewing lines [0 - 41] of 41** | |
| L0: | |
| L1: URL: Search_Results/MS MARCO benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†MS MARCO - Microsoft Open Source; publish_date: none†microsoft.github.io】 | |
| L5: The MS MARCO datasets are intended for non-commercial research purposes only to | |
| L6: promote advancement in the field of artificial intelligence and related areas, | |
| L7: ... | |
| L8: * 【1†microsoft/ms_marco · Datasets at Hugging Face; publish_date: | |
| L9: none†huggingface.co】 Starting with a paper released at NIPS 2016, MS MARCO is a | |
| L10: collection of datasets focused on deep learning in search. The first dataset was | |
| L11: a question ... | |
| L12: * 【2†Benchmarking Ranking Models in the Large-Data Regime; publish_date: | |
| L13: none†arxiv.org】 by N Craswell · 2021 · Cited by 89 — This paper uses the MS | |
| L14: MARCO and TREC Deep Learning Track as our case study, comparing it to the case | |
| L15: of TREC ad hoc ranking in the 1990s. | |
| L16: * 【3†Benchmarking Ranking Models in the Large-Data Regime; publish_date: | |
| L17: none†www.microsoft.com】 This paper uses the MS MARCO and TREC Deep Learning | |
| L18: Track as our case study, comparing it to the case of TREC ad hoc ranking in the | |
| L19: 1990s. We show how the ... | |
| L20: * 【4†Datasets for Document and Passage Ranking Leadboards; publish_date: | |
| L21: none†microsoft.github.io】 The MS MARCO document and passage ranking leaderboards | |
| L22: complements the TREC Deep Learning Track by providing on-going evaluation of | |
| L23: submissions using pre- ... | |
| L24: * 【5†MS MARCO: Benchmarking Ranking Models in the Large- ...; publish_date: | |
| L25: none†dl.acm.org】 Jul 11, 2021 — This paper uses the MS MARCO and TREC Deep | |
| L26: Learning Track as our case study, comparing it to the case of TREC ad hoc | |
| L27: ranking in the 1990s. | |
| L28: * 【6†ir_datasets : MSMARCO (passage); publish_date: none†ir-datasets.com】 A | |
| L29: passage ranking benchmark with a collection of 8.8 million passages and question | |
| L30: queries. Most relevance judgments are shallow. | |
| L31: * 【7†MS MARCO; publish_date: none†sbert.net】 MS MARCO Passage Ranking is a | |
| L32: large dataset to train models for information retrieval. It consists of about | |
| L33: 500k real search queries from Bing search engine ... | |
| L34: * 【8†MS MARCO: A Human Generated MAchine Reading ...; publish_date: | |
| L35: none†arxiv.org】 by P Bajaj · 2016 · Cited by 1151 — We introduce a large scale | |
| L36: MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset | |
| L37: comprises of 1,010,916 anonymized ... | |
| L38: * 【9†MS MARCO Web Search: A Large-scale Information-rich ...; publish_date: | |
| L39: none†www.microsoft.com】 May 13, 2024 — MS MARCO Web Search offers a retrieval | |
| L40: benchmark with three web retrieval challenge tasks that demands innovations in | |
| L41: both machine learning and ...</result> | |
| <result>[13] BEIR benchmark (Search_Results/BEIR benchmark) | |
| **viewing lines [0 - 37] of 37** | |
| L0: | |
| L1: URL: Search_Results/BEIR benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†详细介绍文本检索基准BEIR: A Heterogeneous Benchmark ...; publish_date: | |
| L5: none†blog.csdn.net】 2023年1月1日 — | |
| L6: BEIR旨在为所有不同的检索任务提供一个一站式的零样本评估基准。为了构建一个全面的评估基准，选择方法对于收集具有理想属性的任务和数据集至关重要。对于 ... | |
| L7: * 【1†beir-cellar/beir; publish_date: none†github.com】 BEIR is a heterogeneous | |
| L8: benchmark containing diverse IR tasks. It also provides a common and easy | |
| L9: framework for evaluation of your NLP-based retrieval models ... | |
| L10: * 【2†BEIR: A Heterogenous Benchmark for Zero-shot Evaluation ...; | |
| L11: publish_date: none†arxiv.org】 作者：N Thakur · 2021 · 被引用次数：1480 — We introduce | |
| L12: Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for | |
| L13: information retrieval. | |
| L14: * 【3†BeIR; publish_date: none†huggingface.co】 BEIR (Benchmarking IR) consists | |
| L15: of a homogenous benchmark for diverse sentence or passage level IR tasks. It | |
| L16: provides a common and easy framework for the cross ... | |
| L17: * 【4†论文分享：BEIR A Heterogeneous Benchmark for Zero-shot ...; publish_date: | |
| L18: none†zhuanlan.zhihu.com】 2022年10月3日 — 分享论文，夹带个人理解的分享，建议结合原论文看。 1 研究背景. | |
| L19: 本论文主要关注的领域是query-document检索（下文简称qd检索），即根据query去文档库里 ... | |
| L20: * 【5†Benchmarking IR Information Retrieval (BEIR); publish_date: | |
| L21: none†zilliz.com】 BEIR is a benchmark designed for evaluating the versatility and | |
| L22: robustness of information retrieval models. It features 18 diverse datasets | |
| L23: from domains like ... | |
| L24: * 【6†BEIR (Benchmarking IR) - OpenDataLab; publish_date: none†opendatalab.com】 | |
| L25: 简介-Introduction. BEIR（Benchmarking | |
| L26: IR）是包含不同信息检索（IR）任务的异构基准。通过BEIR，可以系统地研究多种神经检索方法的零样本泛化能力。 | |
| L27: * 【7†What is the BEIR benchmark and how is it used?; publish_date: | |
| L28: none†milvus.io】 The BEIR (Benchmarking Information Retrieval) benchmark is a | |
| L29: standardized framework designed to evaluate the effectiveness of search and | |
| L30: retrieval algorithms. | |
| L31: * 【8†BEIR Benchmark数据集卡片; publish_date: none†www.atyun.com】 BEIR | |
| L32: Benchmark数据集卡片. 数据集简介. BEIR是一个异构评测基准，由18个多样化的数据集构建而成，代表了9个信息检索任务：. 事实查证： FEVER ， | |
| L33: Climate-FEVER ， SciFact ... | |
| L34: * 【9†Evaluating search relevance part 1 - The BEIR benchmark; publish_date: | |
| L35: none†www.elastic.co】 2024年7月16日 — Learn to evaluate your search system in the | |
| L36: context of better understanding the BEIR benchmark, with tips & techniques to | |
| L37: improve your ...</result> | |
| <result>[14] MIRACL benchmark (Search_Results/MIRACL benchmark) | |
| **viewing lines [0 - 41] of 41** | |
| L0: | |
| L1: URL: Search_Results/MIRACL benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†MIRACL | Multilingual Information Retrieval Across a ...; publish_date: | |
| L5: none†project-miracl.github.io】 MIRACL (Multilingual Information Retrieval Across | |
| L6: a Continuum of Languages) is an WSDM 2023 Cup challenge that focuses on search | |
| L7: across 18 different ... | |
| L8: * 【1†project-miracl/miracl: A large-scale multilingual dataset for ...; | |
| L9: publish_date: none†github.com】 A large-scale multilingual dataset for | |
| L10: Information Retrieval. Thorough human-annotations across 18 diverse languages. | |
| L11: * 【2†A Large, multilingual, visual document retrieval benchmark; publish_date: | |
| L12: none†arxiv.org】 by R Osmulski · 2025 · Cited by 2 — MIRACL-VISION is a | |
| L13: challenging, representative, multilingual evaluation benchmark for visual | |
| L14: retrieval pipelines and will help the community build robust ... | |
| L15: * 【3†miracl/miracl · Datasets at Hugging Face; publish_date: | |
| L16: none†huggingface.co】 MIRACL (Multilingual Information Retrieval Across a | |
| L17: Continuum of Languages) is a multilingual retrieval dataset that focuses on | |
| L18: search across 18 different ... | |
| L19: * 【4†MIRACL: A Multilingual Retrieval Dataset Covering 18 ...; publish_date: | |
| L20: none†direct.mit.edu】 by X Zhang · 2023 · Cited by 131 — MIRACL is a multilingual | |
| L21: dataset for ad hoc retrieval across 18 languages that collectively encompass | |
| L22: over three billion native speakers around the world. | |
| L23: * 【5†(PDF) MIRACL-VISION: A Large, multilingual, visual ...; publish_date: | |
| L24: none†www.researchgate.net】 May 23, 2025 — MIRACL-VISION covers 18 languages, and | |
| L25: is an extension of the MIRACL dataset, a popular benchmark to evaluate text- | |
| L26: based multilingual retrieval ... | |
| L27: * 【6†A Large, multilingual, visual document retrieval benchmark; publish_date: | |
| L28: none†arxiv.org】 by R Osmulski · 2025 · Cited by 2 — MIRACL-VISION is a | |
| L29: challenging, representative, multilingual evaluation benchmark for visual | |
| L30: retrieval pipelines and will help the community ... | |
| L31: * 【7†ir_datasets : MIRACL; publish_date: none†ir-datasets.com】 | |
| L32: "miracl/ar/test-a". The held-out test set (version a) for Arabic. | |
| L33: queriesdocsCitationMetadata. 936 queries. Language: ar. Query type: | |
| L34: GenericQuery: (namedtuple). | |
| L35: * 【8†Evaluate on MIRACL — BGE documentation; publish_date: none†bge-model.com】 | |
| L36: MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is | |
| L37: an WSDM 2023 Cup challenge that focuses on search across 18 different languages. | |
| L38: * 【9†MIRACL - Alpha's Tech Garden; publish_date: | |
| L39: none†techgarden.alphasmanifesto.com】 MIRACL (Multilingual Information Retrieval | |
| L40: Across a Continuum of Languages) is a multilingual dataset we have built for the | |
| L41: WSDM 2023 Cup ...</result> | |
| <result>[15] Zero-shot IR benchmark (Search_Results/Zero-shot IR benchmark) | |
| **viewing lines [0 - 40] of 40** | |
| L0: | |
| L1: URL: Search_Results/Zero-shot IR benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†BEIR: A Heterogenous Benchmark for Zero-shot Evaluation ...; | |
| L5: publish_date: none†arxiv.org】 by N Thakur · 2021 · Cited by 1480 — We introduce | |
| L6: Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for | |
| L7: information retrieval.See more | |
| L8: * 【1†beir-cellar/beir; publish_date: none†github.com】 BEIR: A Heterogenous | |
| L9: Benchmark for Zero-shot Evaluation of Information Retrieval Models (NeurIPS | |
| L10: 2021, Datasets and Benchmarks Track); Resources for Brewing ...See more | |
| L11: * 【2†Benchmarking IR Information Retrieval (BEIR); publish_date: | |
| L12: none†zilliz.com】 BEIR is a tool to evaluate how well Information Retrieval | |
| L13: systems perform across many tasks and types of information, and is a standard | |
| L14: benchmark. | |
| L15: * 【3†BEIR: A Heterogeneous Benchmark for Zero-shot ...; publish_date: | |
| L16: none†datasets-benchmarks-proceedings.neurips.cc】 by N Thakur · Cited by 1480 — | |
| L17: BEIR is a robust, heterogeneous benchmark for information retrieval, using 18 | |
| L18: datasets and 9 tasks to evaluate model generalization. | |
| L19: * 【4†BEIR; publish_date: none†eval.ai】 BEIR is a heterogeneous zero-shot | |
| L20: retrieval benchmark containing 18 datasets from diverse text retrieval tasks and | |
| L21: domains.See more | |
| L22: * 【5†[2409.15763] IRSC: A Zero-shot Evaluation Benchmark for ...; | |
| L23: publish_date: none†arxiv.org】 by H Lin · 2024 · Cited by 2 — This paper | |
| L24: introduces the IRSC benchmark for evaluating the performance of embedding models | |
| L25: in multilingual RAG tasks.See more | |
| L26: * 【6†FactIR: A Real-World Zero-shot Open-Domain Retrieval ...; publish_date: | |
| L27: none†dl.acm.org】 May 23, 2025 — In this paper, we present a real-world retrieval | |
| L28: benchmark FactIR, derived from Factiverse production logs, enhanced with human | |
| L29: annotations. We ...See more | |
| L30: * 【7†UniIR: Training and Benchmarking Universal Multimodal ...; publish_date: | |
| L31: none†tiger-ai-lab.github.io】 At test time, we evaluated the zero-shot | |
| L32: performance of all fine-tuned models, as well as SoTA pre-trained retrievers on | |
| L33: the three held-out datasets. UniIR ...See more | |
| L34: * 【8†Zero-Shot BEIR Tasks; publish_date: none†www.emergentmind.com】 Aug 26, | |
| L35: 2025 — Zero-Shot BEIR Tasks are evaluation methodologies that assess IR models' | |
| L36: ability to generalize to unseen query domains without task-specific ...See more | |
| L37: * 【9†BEIR-PL: Zero Shot Information Retrieval Benchmark for ...; publish_date: | |
| L38: none†aclanthology.org】 by K Wojtasik · 2024 · Cited by 12 — BEIR-PL is a new | |
| L39: benchmark with 13 datasets for Polish Information Retrieval, created to advance | |
| L40: research in this area.</result> | |
| <result>[16] WebGPT benchmark (Search_Results/WebGPT benchmark) | |
| **viewing lines [0 - 38] of 38** | |
| L0: | |
| L1: URL: Search_Results/WebGPT benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WebGPT: Improving the factual accuracy of language ...; publish_date: | |
| L5: none†openai.com】 Dec 16, 2021 — Our models outperform GPT‑3 on TruthfulQA and | |
| L6: exhibit more favourable scaling properties. However, our models lag behind human | |
| L7: performance, ... | |
| L8: * 【1†A Simple Yet Challenging Benchmark for Browsing Agents; publish_date: | |
| L9: none†arxiv.org】 by J Wei · 2025 · Cited by 124 — Abstract. We present | |
| L10: BrowseComp, a simple yet challenging benchmark for measuring the ability for | |
| L11: agents to browse the web. | |
| L12: * 【2†openai/webgpt_comparisons · Datasets at Hugging Face; publish_date: | |
| L13: none†huggingface.co】 This is the dataset of all comparisons that were marked as | |
| L14: suitable for reward modeling by the end of the WebGPT project. There are 19,578 | |
| L15: comparisons in total. | |
| L16: * 【3†Evaluation & Limitations of WebGPT, WebVoyager & Agent-E; publish_date: | |
| L17: none†deepsense.ai】 Oct 14, 2024 — WebArena benchmark features 812 tasks | |
| L18: evaluated using metrics such as Exact Match, Must Include, and Fuzzy Match, | |
| L19: focusing on outcomes rather ... | |
| L20: * 【4†OpenAI Announces Question-Answering AI WebGPT; publish_date: | |
| L21: none†www.infoq.com】 Jan 25, 2022 — On the TriviaQA benchmark, WebGPT | |
| L22: outperformed GPT-3, producing answers that were true 75% of the time, and "both | |
| L23: true and informative" 54% of ... | |
| L24: * 【5†WebGPT: Improving the factual accuracy of language models ...; | |
| L25: publish_date: none†kargarisaac.medium.com】 The top-performing model generated | |
| L26: answers that were preferred over 56% of the time compared to answers produced by | |
| L27: human demonstrators, with ... | |
| L28: * 【6†Browser-assisted question-answering with human feedback; publish_date: | |
| L29: none†www.alphaxiv.org】 WebGPT represents a significant advancement in long-form | |
| L30: question answering by combining the language generation capabilities of GPT-3 | |
| L31: with real-time web ... | |
| L32: * 【7†Benchmarking Open-Source Large Language Models, GPT-4 ...; publish_date: | |
| L33: none†ai.nejm.org】 by S Wu · 2024 · Cited by 69 — We show that the current widely | |
| L34: used open-source LLMs have poor zero-shot reasoning ability in nephrology | |
| L35: compared with GPT-4 and Claude 2. | |
| L36: * 【8†0hq/WebGPT: Run GPT model on ...; publish_date: none†github.com】 WebGPT | |
| L37: is a vanilla JS and HTML implementation of a transformer model, intended as a | |
| L38: proof-of-concept as well as educational resource.</result> | |
| <result>[17] WebShop benchmark (Search_Results/WebShop benchmark) | |
| **viewing lines [0 - 41] of 41** | |
| L0: | |
| L1: URL: Search_Results/WebShop benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WebShop: Towards Scalable Real-World Web Interaction ...; publish_date: | |
| L5: none†arxiv.org】 by S Yao · 2022 · Cited by 710 — To bridge this gap, we develop | |
| L6: WebShop -- a simulated e-commerce website environment with 1.18 million real- | |
| L7: world products and 12,087 crowd- ... | |
| L8: * 【1†WebShop; publish_date: none†webshop-pnlp.github.io】 To bridge this gap, | |
| L9: we develop WebShop – a simulated e-commerce website environment with 1.18 | |
| L10: million real-world products and 12,087 crowd-sourced text ... | |
| L11: * 【2†princeton-nlp/WebShop; publish_date: none†github.com】 WebShop is a | |
| L12: simulated e-commerce website environment with 1.18 million real-world products | |
| L13: and 12,087 crowd-sourced text instructions. In this environment, an ... | |
| L14: * 【3†WebShop: Towards Scalable Real-World Web Interaction ...; publish_date: | |
| L15: none†papers.nips.cc】 by S Yao · 2022 · Cited by 710 — We collect over 1,600 | |
| L16: human trajectories to first validate the benchmark, then train and evaluate a | |
| L17: diverse range of agents using reinforcement learning, ... | |
| L18: * 【4†WebShop: Towards Scalable Real-World Web Interaction ...; publish_date: | |
| L19: none†proceedings.neurips.cc】 by S Yao · 2022 · Cited by 709 — We have developed | |
| L20: WebShop, a new web-based benchmark for sequential decision making and language | |
| L21: grounding, modeled on interaction with an e-commerce website. | |
| L22: * 【5†Webshop & Benchmark Analysis | Documentation Infinity; publish_date: | |
| L23: none†docs.fact-finder.com】 Aug 15, 2025 — Evaluation of your shop based on | |
| L24: different categories in comparison, to your competitors/industry. Recommended | |
| L25: when doing a shop relaunch. | |
| L26: * 【6†A Multi-Shop Benchmark for Evaluating Web Agents; publish_date: | |
| L27: none†arxiv.org】 by R Peeters · 2025 · Cited by 2 — Compared to existing | |
| L28: e-commerce benchmarks, such as WebShop or ShoppingBench, WebMall introduces | |
| L29: comparison-shopping tasks across multiple shops ... | |
| L30: * 【7†WebShop: towards scalable real-world web interaction with ...; | |
| L31: publish_date: none†dl.acm.org】 by S Yao · 2022 · Cited by 710 — To bridge this | |
| L32: gap, we develop WebShop - a simulated e-commerce website environment with 1.18 | |
| L33: million real-world products and 12, 087 crowd- ... | |
| L34: * 【8†[PDF] WebShop: Towards Scalable Real-World Web ...; publish_date: | |
| L35: none†www.semanticscholar.org】 It is shown that agents trained on WebShop exhibit | |
| L36: non-trivial sim-to-real transfer when evaluated on amazon.com and ebay.com, | |
| L37: indicating the potential ... | |
| L38: * 【9†X-WebAgentBench: A Multilingual Interactive Web ...; publish_date: | |
| L39: none†aclanthology.org】 by P Wang · 2025 · Cited by 3 — (2023) based on the | |
| L40: English WebShop benchmark (Yao et al., 2022), while the multilingual task scores | |
| L41: are ob- tained through evaluation on our own benchmark.</result> | |
| <result>[18] ALFWorld benchmark (Search_Results/ALFWorld benchmark) | |
| **viewing lines [0 - 31] of 31** | |
| L0: | |
| L1: URL: Search_Results/ALFWorld benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†ALFWorld; publish_date: none†alfworld.github.io】 ALFWorld contains | |
| L5: interactive TextWorld environments (Côté et. al) that parallel embodied worlds | |
| L6: in the ALFRED dataset (Shridhar et. al). | |
| L7: * 【1†ALFWorld: Aligning Text and Embodied Environments for ...; publish_date: | |
| L8: none†arxiv.org】 by M Shridhar · 2020 · Cited by 674 — ALFWorld enables the | |
| L9: creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, | |
| L10: corresponds directly to concrete, visually grounded actions. | |
| L11: * 【2†ALFWorld: Aligning Text and Embodied Environments ...; publish_date: | |
| L12: none†github.com】 ALFWorld contains interactive TextWorld environments (Côté et. | |
| L13: al) that parallel embodied worlds in the ALFRED dataset (Shridhar et. al). | |
| L14: * 【3†alfworld - benchmark's activity; publish_date: none†huggingface.co】 MM- | |
| L15: IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models Paper | |
| L16: • 2502.00698 • Published Feb 1 • 24 | |
| L17: * 【4†Tackling AlfWorld with Action Attention and Common ...; publish_date: | |
| L18: none†neurips.cc】 On the Alfworld benchmark for indoor instruction following, we | |
| L19: achieve a significantly higher success rate (50% over the baseline) with our | |
| L20: novel object ... | |
| L21: * 【5†ALFWORLD: ALIGNING TEXT AND EMBODIED ...; publish_date: | |
| L22: none†openreview.net】 by M Shridhar · Cited by 674 — The ALFRED dataset (Shridhar | |
| L23: et al., 2020), set in the THOR simulator (Kolve et al., 2017), is a benchmark | |
| L24: for learning to com- plete embodied household tasks ... | |
| L25: * 【6†AlfWorld; publish_date: none†primo.ai】 Mar 23, 2024 — A simulator that | |
| L26: enables agents to learn abstract, text based policies in TextWorld (Côté et al., | |
| L27: 2018) and then execute goals from the ALFRED benchmark. | |
| L28: * 【7†AlfWorld performance across 134 tasks showing cumulative...; | |
| L29: publish_date: none†www.researchgate.net】 In the AlfWorld benchmark, we defined | |
| L30: hallucination as the occurrence of two or more consecutive identical actions in | |
| L31: which the environment responded with ...</result> | |
| <result>[19] Mind2Web benchmark (Search_Results/Mind2Web benchmark) | |
| **viewing lines [0 - 40] of 40** | |
| L0: | |
| L1: URL: Search_Results/Mind2Web benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†Mind2Web: Towards a Generalist Agent for the Web; publish_date: none†osu- | |
| L5: nlp-group.github.io】 Mind2Web is a dataset for developing and evaluating | |
| L6: generalist agents for the web that can follow language instructions to complete | |
| L7: complex tasks on any ... | |
| L8: * 【1†Online-Mind2Web Leaderboard; publish_date: none†huggingface.co】 Online- | |
| L9: Mind2Web is a benchmark designed to evaluate the real-world performance of web | |
| L10: agents on live websites, featuring 300 tasks across 136 popular sites ... | |
| L11: * 【2†Mind2Web: Towards a Generalist Agent for the Web; publish_date: | |
| L12: none†github.com】 Mind2Web is the first dataset for developing and evaluating | |
| L13: generalist agents for the web that can follow language instructions to complete | |
| L14: complex tasks on any ... | |
| L15: * 【3†HAL: Online Mind2Web Leaderboard; publish_date: | |
| L16: none†hal.cs.princeton.edu】 Online Mind2Web leaderboard for evaluating AI agents' | |
| L17: ability to complete tasks on real, changing webpages. | |
| L18: * 【4†[2506.21506] Mind2Web 2: Evaluating Agentic Search with ...; | |
| L19: publish_date: none†arxiv.org】 by B Gou · 2025 · Cited by 11 — In this paper, we | |
| L20: introduce Mind2Web 2, a benchmark of 130 realistic, high-quality, and long- | |
| L21: horizon tasks that require real-time web browsing and extensive ... | |
| L22: * 【5†Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge; | |
| L23: publish_date: none†osu-nlp-group.github.io】 We introduce Mind2Web 2, a benchmark | |
| L24: of 130 realistic, high-quality, long-horizon tasks that require real-time web | |
| L25: browsing and extensive information ... | |
| L26: * 【6†Mind2Web: The Benchmark for AI Agent Evaluation and ...; publish_date: | |
| L27: none†www.enhans.ai】 Sep 26, 2025 — Mind2Web is a globally recognized web-based | |
| L28: AI Agent evaluation benchmark introduced by the NLP group at Ohio State | |
| L29: University at NeurIPS 2023. | |
| L30: * 【7†Evaluating AI Web Agents: Insights from the WebCanvas ...; publish_date: | |
| L31: none†medium.com】 Thanks to the comprehensive WebCanvas Benchmark, which | |
| L32: incorporates a robust Mind2Web-Live data set of 542 live web tasks and 2,439 ... | |
| L33: * 【8†Mind2Web: Towards a Generalist Agent for the Web; publish_date: | |
| L34: none†proceedings.neurips.cc】 by X Deng · 2023 · Cited by 760 — We introduce | |
| L35: Mind2Web, the first dataset for developing and evaluating generalist agents for | |
| L36: the web that can follow language instructions to complete complex ... | |
| L37: * 【9†Mind2Web: Towards a Generalist Agent for the Web; publish_date: | |
| L38: none†openreview.net】 by X Deng · Cited by 760 — We introduce Mind2Web, the first | |
| L39: dataset for developing and evaluating generalist agents for the web that can | |
| L40: follow language instructions to complete complex ...</result> | |
| <result>[20] VisualWebArena benchmark (Search_Results/VisualWebArena benchmark) | |
| **viewing lines [0 - 38] of 38** | |
| L0: | |
| L1: URL: Search_Results/VisualWebArena benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†VisualWebArena is a benchmark for multimodal agents.; publish_date: | |
| L5: none†github.com】 VisualWebArena is a realistic and diverse benchmark for | |
| L6: evaluating multimodal autonomous language agents. | |
| L7: * 【1†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date: | |
| L8: none†arxiv.org】 by JY Koh · 2024 · Cited by 363 — To bridge this gap, we | |
| L9: introduce VisualWebArena, a benchmark designed to assess the performance of | |
| L10: multimodal web agents on realistic \textit{ ... | |
| L11: * 【2†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date: | |
| L12: none†jykoh.com】 To bridge this gap, we introduce VisualWebArena, a benchmark | |
| L13: designed to assess the performance of multimodal web agents on realistic | |
| L14: visually grounded tasks. | |
| L15: * 【3†VisualWebArena: Evaluating Multimodal Agents on ...; publish_date: | |
| L16: none†arxiv.org】 VisualWebArena is a research benchmark to measure and evaluate | |
| L17: the progress of multimodal agents. It is primarily meant to act as a self- | |
| L18: contained sandbox ... | |
| L19: * 【4†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date: | |
| L20: none†aclanthology.org】 by JY Koh · 2024 · Cited by 363 — To bridge this gap, we | |
| L21: introduce VisualWebArena, a benchmark designed to assess the performance of | |
| L22: multimodal web agents on *realistic visually grounded tasks*. | |
| L23: * 【5†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date: | |
| L24: none†www.semanticscholar.org】 VisualWebArena: Evaluating Multimodal Agents on | |
| L25: Realistic Visual Web Tasks ... MMInA, a multihop and multimodal benchmark to | |
| L26: evaluate the embodied agents ... | |
| L27: * 【6†CMU Researchers Introduce VisualWebArena: An AI ...; publish_date: | |
| L28: none†www.marktechpost.com】 Feb 9, 2024 — VisualWebArena, a benchmark designed | |
| L29: and developed to evaluate the performance of multimodal web agents on realistic | |
| L30: and visually stimulating challenges. | |
| L31: * 【7†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date: | |
| L32: none†www.themoonlight.io】 The paper "VisualWebArena: Evaluating Multimodal | |
| L33: Agents on Realistic Visually Grounded Web Tasks" introduces a new benchmark, | |
| L34: **VisualWebArena**, ... | |
| L35: * 【8†WebArena: A Realistic Web Environment for Building ...; publish_date: | |
| L36: none†webarena.dev】 Our benchmark is implemented in our fully interactable | |
| L37: highly-realistic WebArena environment. It features diverse tasks human may | |
| L38: encounter in their daily ...</result> | |
| <result>[21] SearchBench benchmark (Search_Results/SearchBench benchmark) | |
| **viewing lines [0 - 40] of 40** | |
| L0: | |
| L1: URL: Search_Results/SearchBench benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†Talc-AI/search-bench; publish_date: none†github.com】 A practical | |
| L5: benchmark that focuses on every day helpfulness of LLM products, not just the | |
| L6: underlying models. Searchbench is a benchmark that addresses these ... | |
| L7: * 【1†Evaluating LLMs' Ability to Reason About Search Problems; publish_date: | |
| L8: none†arxiv.org】 These capabilities are essential for robust reasoning, making | |
| L9: SearchBench a valuable benchmark for evaluating LLMs' reasoning capabilities as | |
| L10: they continue to ... | |
| L11: * 【2†NasimBrz/SearchBench · Datasets at Hugging Face; publish_date: | |
| L12: none†huggingface.co】 Dataset Summary. SearchBench is a benchmark designed to | |
| L13: evaluate Language Models' (LLMs) ability to solve state-based problems that | |
| L14: require combinatorial search ... | |
| L15: * 【3†Evaluating LLMs' Ability to Reason About Search Problems; publish_date: | |
| L16: none†openreview.net】 2025年10月22日 — To further investigate this, we introduce a | |
| L17: new benchmark, SearchBench, which contains 11 unique search problems inspired by | |
| L18: intuitive puzzles. | |
| L19: * 【4†Navigating the Labyrinth: Evaluating and Enhancing LLMs' ...; | |
| L20: publish_date: none†hub.baai.ac.cn】 2024年6月17日 — | |
| L21: 论文提出了一个新的基准测试SearchBench，包含11种独特的搜索问题类型，并自动化生成任意数量的实例和分析解决方案的可行性、正确性和最优性。论文使用A* | |
| L22: ... | |
| L23: * 【5†Towards Unified Text-based Person Retrieval: A Large- ...; publish_date: | |
| L24: none†blog.csdn.net】 2023年10月17日 — ... Search | |
| L25: Benchmark（面向统一的基于文本的人物检索：一个大规模的多属性和语言搜索基准）; 研究背景. 相关工作; BENCHMARK. 论文方法分析. 网络框架; | |
| L26: 1、APTM ... | |
| L27: * 【6†Desearch-ai/ai-search-benchmark; publish_date: none†github.com】 The | |
| L28: SearchBench repository addresses common issues with traditional benchmarks by | |
| L29: focusing on practical, everyday use cases rather than theoretical limits. It ... | |
| L30: * 【7†o1 results for 3 benchmarks: PlanBench, SearchBench, ...; publish_date: | |
| L31: none†www.reddit.com】 o1 results for 3 benchmarks: PlanBench, SearchBench, and | |
| L32: Summary of a Haystack. AI. PlanBench: Paper "LLMs Still Can't Plan; Can LRMs? A | |
| L33: ... | |
| L34: * 【8†Evaluating LLMs' Ability to Reason About Search Problems; publish_date: | |
| L35: none†ui.adsabs.harvard.edu】 To further investigate this, we introduce a new | |
| L36: benchmark, SearchBench, which contains 11 unique search problems inspired by | |
| L37: intuitive puzzles. Each SearchBench ... | |
| L38: * 【9†Introducing SearchBench; publish_date: none†www.tag1consulting.com】 | |
| L39: Toward this goal, over the weekend I launched a new project called SearchBench, | |
| L40: a Drupal module for benchmarking Drupal's search performance. As the module ...</result> | |
| <result>[22] WebVLN benchmark (Search_Results/WebVLN benchmark) | |
| **viewing lines [0 - 42] of 42** | |
| L0: | |
| L1: URL: Search_Results/WebVLN benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WebVLN: Vision-and-Language Navigation on Websites; publish_date: | |
| L5: none†ojs.aaai.org】 by Q Chen · 2024 · Cited by 35 — the WebVLN-v1 dataset, where | |
| L6: the performance is far from saturation, highlighting the utility of our | |
| L7: WebVLN-v1 as a benchmark to assess progress in this field. | |
| L8: * 【1†[2312.15820] WebVLN: Vision-and-Language Navigation on Websites; | |
| L9: publish_date: none†ar5iv.labs.arxiv.org】 Experimental results show that WebVLN- | |
| L10: Net outperforms current VLN and web-related navigation methods. ... Code is | |
| L11: available at: https://github.com/WebVLN/WebVLN. | |
| L12: * 【2†WebVLN: Vision-and-Language Navigation on Websites; publish_date: | |
| L13: none†github.com】 Experimental results show that WebVLN-Net outperforms current | |
| L14: VLN and web-related navigation methods. We believe that the introduction of the | |
| L15: new WebVLN task ... | |
| L16: * 【3†Vision-and-Language Navigation in the Real-World; publish_date: | |
| L17: none†digital.library.adelaide.edu.au】 By leveraging our proposed WebVLN-v1 | |
| L18: dataset, experimental results showcase the superior performance of WebVLN-Net | |
| L19: compared to existing VLN and web-related ... | |
| L20: * 【4†WebVLN: Vision-and-Language Navigation on Websites; publish_date: | |
| L21: none†www.researchgate.net】 Experimental results show that WebVLN-Net outperforms | |
| L22: current VLN and web-related navigation methods. We believe that the | |
| L23: introduction of the newWebVLN task and ... | |
| L24: * 【5†[PDF] WebVLN: Vision-and-Language Navigation on Websites; publish_date: | |
| L25: none†www.semanticscholar.org】 A new task named Vision-and-Language Navigation on | |
| L26: Websites (WebVLN), where question-based instructions are used to train an | |
| L27: agent, emulating how users ... | |
| L28: * 【6†WebVLN: Vision-and-Language Navigation on Websites; publish_date: | |
| L29: none†arxiv.org】 by Q Chen · 2023 · Cited by 35 — Experimental results show that | |
| L30: WebVLN-Net outperforms current VLN and web-related navigation methods. We | |
| L31: believe that the introduction of the ... | |
| L32: * 【7†Human-Aware Vision-and-Language Navigation; publish_date: | |
| L33: none†proceedings.neurips.cc】 by H Li · 2024 · Cited by 19 — Vision-and-Language | |
| L34: Navigation (VLN) [2, 7, 9, 40] has emerged as a key benchmark for evaluating. | |
| L35: Sim2Real transfer [23], showing impressive performance in ... | |
| L36: * 【8†LiveBench; publish_date: none†livebench.ai】 Introducing LiveBench: a | |
| L37: benchmark for LLMs designed with test set contamination and objective evaluation | |
| L38: in mind. | |
| L39: * 【9†MG-VLN: Benchmarking Multi-Goal and Long-Horizon ...; publish_date: | |
| L40: none†ieeexplore.ieee.org】 by J Zhang · 2024 — This task aims to provide a | |
| L41: simulation benchmark to guide the design of lifelong and long-horizon navigation | |
| L42: robots.</result> | |
| <result>[23] WebNav benchmark (Search_Results/WebNav benchmark) | |
| **viewing lines [0 - 36] of 36** | |
| L0: | |
| L1: URL: Search_Results/WebNav benchmark | |
| L2: # Search Results | |
| L3: | |
| L4: * 【0†WebNav: A New Large-Scale Task for Natural Language ...; publish_date: | |
| L5: none†github.com】 WebNav is a benchmark task for evaluating an agent with | |
| L6: abilities to understand natural language and plan on partially observed | |
| L7: environments. | |
| L8: * 【1†[1602.02261] End-to-End Goal-Driven Web Navigation; publish_date: | |
| L9: none†arxiv.org】 by R Nogueira · 2016 · Cited by 39 — We propose a goal-driven | |
| L10: web navigation as a benchmark task for evaluating an agent with abilities to | |
| L11: understand natural language and plan on partially ... | |
| L12: * 【2†nyu-dl/dl4ir-webnav; publish_date: none†github.com】 WebNav is a benchmark | |
| L13: task for evaluating an agent with abilities to understand natural language and | |
| L14: plan on partially observed environments. | |
| L15: * 【3†WebNav: A New Large-Scale Task for Natural Language ...; publish_date: | |
| L16: none†www.researchgate.net】 We propose a goal-driven web navigation as a | |
| L17: benchmark task for evaluating an agent with abilities to understand natural | |
| L18: language and plan on partially ... | |
| L19: * 【4†WebNav: An Intelligent Agent for Voice-Controlled Web ...; publish_date: | |
| L20: none†arxiv.org】 In this paper, we introduce WebNav, an innovative multi-modal | |
| L21: agent designed to automate web tasks based on natural language. | |
| L22: * 【5†WebCanvas: Benchmarking Web Agents in Online ...; publish_date: | |
| L23: none†openreview.net】 by Y Pan · Cited by 78 — TL;DR: We introduce WebCanvas, an | |
| L24: online evaluation framework for web agents designed to address the dynamic | |
| L25: nature of web interactions. | |
| L26: * 【6†WebNav: An Intelligent Agent for Voice-Controlled Web ...; publish_date: | |
| L27: none†huggingface.co】 Mar 17, 2025 — Preliminary evaluations show that WebNav | |
| L28: outperforms traditional screen readers in response time and task completion | |
| L29: accuracy for the visually ... | |
| L30: * 【7†Web Bench - A new way to compare AI Browser Agents; publish_date: | |
| L31: none†www.skyvern.com】 May 29, 2025 — TL;DR: Web Bench is a new dataset to | |
| L32: evaluate web browsing agents that consists of 5,750 tasks on 452 different | |
| L33: websites, with 2,454 tasks ... | |
| L34: * 【8†Web Framework Benchmarks; publish_date: none†www.techempower.com】 In the | |
| L35: following tests, we have measured the performance of several web application | |
| L36: platforms, full-stack frameworks, and micro-frameworks.</result> | |
| </function_results> | |
| <think>我们收到了大量关于各种基准测试的搜索结果。现在，我们需要整理一份针对搜索智能体的基准测试列表。这些基准测试可以分为几类：网络浏览智能体基准测试（例如BrowseComp、WebVoyager、WebArena、VisualWebArena、Mind2Web、WebVLN、WebNav）、工具使用智能体基准测试（例如ToolBench、GTA）、问答/检索基准测试（例如HotpotQA、FEVER、TriviaQA、Natural Questions、MS MARCO、BEIR、MIRACL），以及特定领域的搜索基准测试（例如HSCodeComp、WebShop、ALFWorld）。此外，还有一些通用智能体基准测试，例如GAIA、SuperCLUE-Agent等。用户要求“详细介绍各自的特点、使用场景、例题”。因此，我需要为每个基准测试提供详细的描述，包括其特点、使用场景，并举例说明。为了全面获取这些信息，我将同时打开以下基准测试的相关页面：WebVoyager（索引3）、WebArena（索引1）、GAIA（索引1）、ToolBench（索引0）、HotpotQA（索引0）、FEVER（索引1）、TriviaQA（索引0）、Natural Questions（索引0）、MS MARCO（索引0）、BEIR（索引1）、MIRACL（索引0）、WebShop（索引0）、ALFWorld（索引0）、Mind2Web（索引0）、VisualWebArena（索引0）、SearchBench（索引1）、WebVLN（索引1）和WebNav（索引0）。</think> | |
| <｜DSML｜function_calls> | |
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| <function_results> | |
| <result>[24] (https://ar5iv.org/abs/2401.13919) | |
| **viewing lines [0 - 129] of 825** | |
| L0: | |
| L1: URL: https://ar5iv.org/abs/2401.13919 | |
| L2: # WebVoyager : Building an End-to-End Web Agent with | |
| L3: Large Multimodal Models | |
| L4: | |
| L5: Hongliang He1,3 , Wenlin Yao2, Kaixin Ma2, Wenhao Yu2, Yong Dai2, | |
| L6: | |
| L7: Hongming Zhang2, | |
| L8: Zhenzhong Lan3, | |
| L9: Dong Yu2 | |
| L10: 1Zhejiang University, | |
| L11: 2Tencent AI Lab, | |
| L12: 3Westlake University | |
| L13: hehongliang@westlake.edu.cn, wenlinyao@global.tencent.com | |
| L14: Work done during the internship at Tencent AI Lab. | |
| L15: | |
| L16: ###### Abstract | |
| L17: | |
| L18: The advancement of large language models (LLMs) leads to a new era marked by the | |
| L19: development of autonomous applications in the real world, which drives | |
| L20: innovation in the creation of advanced web-based agents. Existing web agents | |
| L21: typically only handle one input modality and are evaluated only in simplified | |
| L22: web simulators or static web snapshots, greatly limiting their applicability in | |
| L23: real-world scenarios. To bridge this gap, we introduce WebVoyager, an innovative | |
| L24: Large Multimodal Model (LMM) powered web agent that can complete user | |
| L25: instructions end-to-end by interacting with real-world websites. Moreover, we | |
| L26: propose a new evaluation protocol for web agents to address the challenges of | |
| L27: automatic evaluation of open-ended web agent tasks, leveraging the robust | |
| L28: multimodal comprehension capabilities of GPT-4V. We create a new benchmark by | |
| L29: gathering real-world tasks from 15 widely used websites to evaluate our agents. | |
| L30: We show that WebVoyager achieves a 55.7% task success rate, significantly | |
| L31: surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text- | |
| L32: only) setups, underscoring the exceptional capability of WebVoyager in practical | |
| L33: applications. We found that our proposed automatic evaluation achieves 85.3% | |
| L34: agreement with human judgment, paving the way for further development of web | |
| L35: agents in a real-world setting.111Our code and data will be released at | |
| L36: https://github.com/MinorJerry/WebVoyager | |
| L37: | |
| L38: ## 1 Introduction | |
| L39: | |
| L40: The recent advancement of large language models (LLMs), such as ChatGPT and | |
| L41: GPT-4 (OpenAI, 2023), have sparked significant interest in developing LLM-based | |
| L42: autonomous agents (AutoGPT, 2022) for complex task execution (Qin et al., 2023; | |
| L43: Schick et al., 2023). Recent studies have explored the construction of text- | |
| L44: based web browsing environments and how to instruct large language model agents | |
| L45: to perform web navigation (Nakano et al., 2021; Gur et al., 2023; Zhou et al., | |
| L46: 2023; Lu et al., 2023). The primary challenge in these works lies in managing | |
| L47: complex and verbose HTML texts, and solutions include simplifying and | |
| L48: structuring HTML (Nakano et al., 2021; Zhou et al., 2023; Gur et al., 2023; Deng | |
| L49: et al., 2023). | |
| L50: | |
| L51: However, existing approaches overlook a critical functionality of browsing: | |
| L52: rendering HTML into visual webpages. Particularly, vision capability is crucial | |
| L53: for utilizing tools like web browsers, as rendered web pages are inherently | |
| L54: designed with user experience (UX), emphasizing intuitive information and | |
| L55: structured presentation. This design principle of rendering makes visual | |
| L56: analysis more effective than mere HTML representation. At present, large | |
| L57: multimodal models (LMMs), particularly GPT-4V(ision) (OpenAI, 2023) and Gemini | |
| L58: (Team et al., 2023), demonstrate a remarkable ability to integrate intricate | |
| L59: visual cues with textual information. Existing studies such as Pix2Struct (Lee | |
| L60: et al., 2023) and WebArena (Zhou et al., 2023), have initiated explorations into | |
| L61: using screenshots as inputs for decision-making in web navigation, yet these | |
| L62: are preliminary and do not represent a deep exploration. Therefore, building | |
| L63: multimodal web agents to leverage the environment rendered by browsers through | |
| L64: screenshots, thus mimicking human web browsing behavior, is now a viable | |
| L65: approach to enhance web navigation efficiency. | |
| L66: | |
| L67: We introduce WebVoyager, a multimodal web agent designed to handle web tasks | |
| L68: online in an end-to-end manner, which denotes managing the process from start to | |
| L69: finish autonomously without intermediate human intervention. We construct an | |
| L70: online environment using Selenium for WebVoyager, feeding it with screenshots | |
| L71: and textual content in interactive web elements. Inspired by Set-of-Mark | |
| L72: Prompting (Yang et al., 2023a), we mark interactive web elements on screenshots | |
| L73: (see Figure 2) to facilitate decision-making for WebVoyager. As a pioneer in | |
| L74: combining vision and text information during web navigation, we advocate that | |
| L75: autonomous end-to-end task completion, multimodal capabilities and online | |
| L76: navigation constitute the essential trajectory toward the genuine intelligence | |
| L77: of web agents. | |
| L78: | |
| L79: Another challenge arises when it comes to evaluating an end-to-end web agent | |
| L80: with online navigation. Existing benchmarks, such as Mind2Web (Deng et al., | |
| L81: 2023), primarily focus on stepwise and offline evaluation, where agents follow | |
| L82: predefined “golden” trajectory for action selection. This approach, however, may | |
| L83: not fully account for the variety of viable strategies to accomplish a task, as | |
| L84: it only reflects one possible plan. This limitation could lead to a biased | |
| L85: evaluation and difficulties in fairly comparing different methods. To more | |
| L86: accurately gauge the capabilities of web agents in end-to-end task completion, | |
| L87: we save screenshots throughout the online navigation process, and then use | |
| L88: GPT-4V to evaluate these trajectories and the final results automatically. Human | |
| L89: evaluations are also conducted to verify the results and confirm the | |
| L90: reliability of GPT-4V as the evaluator. | |
| L91: | |
| L92: We conduct evaluations on a collected dataset, which is semi-automatically | |
| L93: generated using a self-instruct (Wang et al., 2022) method, comprising 300 web | |
| L94: tasks from 15 commonly accessed websites. Additionally, we extract 90 web- | |
| L95: related tasks of level 1 and level 2 from the GAIA (Mialon et al., 2023) to | |
| L96: enrich our evaluation. We compare our WebVoyager with 1) GPT-4 (All | |
| L97: Tools)222GPT-4 (All Tools) is an integrated tool-based agent released by OpenAI | |
| L98: in Oct. 2023. See https://chat.openai.com/, and 2) WebVoyager in a text-only | |
| L99: setting, employing the accessibility tree proposed in WebArena (Zhou et al., | |
| L100: 2023) to describe web pages. The results show that WebVoyager achieves a Task | |
| L101: Success Rate of 55.7%, significantly outperforming GPT-4 (All Tools) with a rate | |
| L102: of 32.7% and the text-only setting with a rate of 39.0%, demonstrating the | |
| L103: effectiveness of our method. Furthermore, we report the consistency between | |
| L104: human-human and human-GPT4V to ensure credibility. Our main contributions are as | |
| L105: follows: | |
| L106: | |
| L107: - • | |
| L108: We employ a multimodal web agent that integrates textual and visual information | |
| L109: to address web tasks end-to-end and introduce a generalist planning approach for | |
| L110: navigation. | |
| L111: | |
| L112: - • | |
| L113: We build an online web browsing environment, offering a variety of tasks | |
| L114: centered on widely used websites and introducing a method for expanding these | |
| L115: tasks. | |
| L116: | |
| L117: - • | |
| L118: We conduct manual evaluations of navigation trajectories and propose an | |
| L119: automated evaluation protocol using GPT-4V. We present a comprehensive analysis | |
| L120: of the evaluation results and show that GPT-4V can serve as a reliable evaluator | |
| L121: for online agents. | |
| L122: | |
| L123: ## 2 Related Work | |
| L124: | |
| L125: ### 2.1 Web Agents | |
| L126: | |
| L127: Autonomous web navigation (Shi et al., 2017; Yang et al., 2023b) requires an | |
| L128: agent to follow instructions, construct plans, comprehend complex web | |
| L129: structures, and decompose tasks into step-by-step decisions (Weng, 2023). To </result> | |
| <result>[25] (https://ar5iv.org/abs/2307.13854) | |
| **viewing lines [0 - 124] of 1092** | |
| L0: | |
| L1: URL: https://ar5iv.org/abs/2307.13854 | |
| L2: # WebArena: A Realistic Web Environment for Building Autonomous Agents | |
| L3: | |
| L4: Shuyan Zhou Frank F. Xu11footnotemark: 1 Hao Zhu Xuhui Zhou22footnotemark: 2 | |
| L5: | |
| L6: Robert Lo22footnotemark: 2 Abishek Sridhar22footnotemark: 2 Xianyi Cheng Tianyue | |
| L7: Ou | |
| L8: Yonatan Bisk Daniel Fried Uri Alon Graham Neubig | |
| L9: Carnegie Mellon University | |
| L10: {shuyanzh, fangzhex, gneubig}@cs.cmu.edu | |
| L11: Lead contributors.Equal contribution. | |
| L12: | |
| L13: ###### Abstract | |
| L14: | |
| L15: With advances in generative AI, there is now potential for autonomous agents to | |
| L16: manage daily tasks via natural language commands. However, current agents are | |
| L17: primarily created and tested in simplified synthetic environments, leading to a | |
| L18: disconnect with real-world scenarios. In this paper, we build an environment for | |
| L19: language-guided agents that is highly realistic and reproducible. Specifically, | |
| L20: we focus on agents that perform tasks on the web, and create an environment | |
| L21: with fully functional websites from four common domains: e-commerce, social | |
| L22: forum discussions, collaborative software development, and content management. | |
| L23: Our environment is enriched with tools (e.g., a map) and external knowledge | |
| L24: bases (e.g., user manuals) to encourage human-like task-solving. Building upon | |
| L25: our environment, we release a set of benchmark tasks focusing on evaluating the | |
| L26: functional correctness of task completions. The tasks in our benchmark are | |
| L27: diverse, long-horizon, and designed to emulate tasks that humans routinely | |
| L28: perform on the internet. We experiment with several baseline agents, integrating | |
| L29: recent techniques such as reasoning before acting. The results demonstrate that | |
| L30: solving complex tasks is challenging: our best GPT-4-based agent only achieves | |
| L31: an end-to-end task success rate of 14.41%, significantly lower than the human | |
| L32: performance of 78.24%. These results highlight the need for further development | |
| L33: of robust agents, that current state-of-the-art large language models are far | |
| L34: from perfect performance in these real-life tasks, and that WebArena can be used | |
| L35: to measure such progress. | |
| L36: | |
| L37: Our code, data, environment reproduction resources, and video demonstrations are | |
| L38: publicly available at https://webarena.dev/. | |
| L39: | |
| L40: ## 1 Introduction | |
| L41: | |
| L42: Autonomous agents that perform everyday tasks via human natural language | |
| L43: commands could significantly augment human capabilities, improve efficiency, and | |
| L44: increase accessibility. Nonetheless, to fully leverage the power of autonomous | |
| L45: agents, it is crucial to understand their behavior within an environment that is | |
| L46: both authentic and reproducible. This will allow measurement of the ability of | |
| L47: agents on tasks that human users care about in a fair and consistent manner. | |
| L48: | |
| L49: Current environments for evaluate agents tend to over-simplify real-world | |
| L50: situations. As a result, the functionality of many environments is a limited | |
| L51: version of their real-world counterparts, leading to a lack of task diversity | |
| L52: (Shi et al., 2017; Anderson et al., 2018; Gordon et al., 2018; Misra et al., | |
| L53: 2016; Shridhar et al., 2020; 2021; Yao et al., 2022a). In addition, these | |
| L54: simplifications often lower the complexity of tasks as compared to their | |
| L55: execution in the real world (Puig et al., 2018; Shridhar et al., 2020; Yao et | |
| L56: al., 2022a). Finally, some environments are presented as a static resource (Shi | |
| L57: et al., 2017; Deng et al., 2023) where agents are confined to accessing only | |
| L58: those states that were previously cached during data collection, thus limiting | |
| L59: the breadth and diversity of exploration. Dor evaluation, many environments | |
| L60: focus on comparing the textual surface form of the predicted action sequences | |
| L61: with reference action sequences, disregarding the functional correctness of the | |
| L62: executions and possible alternative solutions (Puig et al., 2018; Jernite et | |
| L63: al., 2019; Xu et al., 2021; Li et al., 2020; Deng et al., 2023). These | |
| L64: limitations often result in a discrepancy between simulated environments and the | |
| L65: real world, and can potentially impact the generalizability of AI agents to | |
| L66: successfully understand, adapt, and operate within complex real-world | |
| L67: situations. | |
| L68: | |
| L69: We introduce WebArena, a realistic and reproducible web environment designed to | |
| L70: facilitate the development of autonomous agents capable of executing tasks (§2). | |
| L71: An overview of WebArena is in Figure 1. Our environment comprises four fully | |
| L72: operational, self-hosted web applications, each representing a distinct domain | |
| L73: prevalent on the internet: online shopping, discussion forums, collaborative | |
| L74: development, and business content management. Furthermore, WebArena incorporates | |
| L75: several utility tools, such as map, calculator, and scratchpad, to best support | |
| L76: possible human-like task executions. Lastly, WebArena is complemented by an | |
| L77: extensive collection of documentation and knowledge bases that vary from general | |
| L78: resources like English Wikipedia to more domain-specific references, such as | |
| L79: manuals for using the integrated development tool (Fan et al., 2022). The | |
| L80: content populating these websites is extracted from their real-world | |
| L81: counterparts, preserving the authenticity of the content served on each | |
| L82: platform. We deliver the hosting services using Docker containers with gym-APIs | |
| L83: (Brockman et al., 2016), ensuring both the usability and the reproducibility of | |
| L84: WebArena. | |
| L85: | |
| L86: Along with WebArena, we release a ready-to-use benchmark with 812 long-horizon | |
| L87: web-based tasks (§3). Each task is described as a high-level natural language | |
| L88: intent, emulating the abstract language usage patterns typically employed by | |
| L89: humans (Bisk et al., 2019). Two example intents are shown in the upper left of | |
| L90: Figure 1. We focus on evaluating the functional correctness of these tasks, | |
| L91: i.e., does the result of the execution actually achieve the desired goal (§3.2). | |
| L92: For instance, to evaluate the example in Figure 2, our evaluation method | |
| L93: verifies the concrete contents in the designated repository. This evaluation is | |
| L94: not only more reliable (Zhong et al., 2017; Chen et al., 2021; Wang et al., | |
| L95: 2022) than comparing the textual surface-form action sequences (Puig et al., | |
| L96: 2018; Deng et al., 2023) but also accommodate a range of potential valid paths | |
| L97: to achieve the same goal, which is a ubiquitous phenomenon in sufficiently | |
| L98: complex tasks. | |
| L99: | |
| L100: We use this benchmark to evaluate several agents that can follow NL command and | |
| L101: perform web-based tasks (§4). These agents are implemented in a few-shot in- | |
| L102: context learning fashion with powerful large language models (LLMs) such as | |
| L103: GPT-4 and PALM-2. Experiment results show that the best GPT-4 agent performance | |
| L104: is somewhat limited, with an end-to-end task success rate of only 14.41%, while | |
| L105: the human performance is 78.24%. We hypothesize that the limited performance of | |
| L106: current LLMs stems from a lack of crucial capabilities such as active | |
| L107: exploration and failure recovery to successfully perform complex tasks (§5.2). | |
| L108: These outcomes underscore the necessity for further development towards robust | |
| L109: and effective agents (LeCun, 2022) in WebArena. | |
| L110: | |
| L111: ## 2 WebArena: Websites as an Environment for Autonomous Agents | |
| L112: | |
| L113: Our goal is to create a realistic and reproducible web environment. We achieve | |
| L114: reproducibility by making the environment standalone, without relying on live | |
| L115: websites. This circumvents technical challenges such as bots being subject to | |
| L116: CAPTCHAs, unpredictable content modifications, and configuration changes, which | |
| L117: obstruct a fair comparison across different systems over time. We achieve | |
| L118: realism by using open-source libraries that underlie many in-use sites from | |
| L119: several popular categories and importing data to our environment from their | |
| L120: real-world counterparts. | |
| L121: | |
| L122: ### 2.1 Controlling Agents through High-level Natural Language | |
| L123: | |
| L124: The WebArena environment is denoted asℰ\mathcal{E} with state space </result> | |
| <result>[26] (https://ar5iv.org/abs/2311.12983) | |
| **viewing lines [0 - 118] of 1207** | |
| L0: | |
| L1: URL: https://ar5iv.org/abs/2311.12983 | |
| L2: 1]FAIR, Meta 2]HuggingFace 3]AutoGPT 4]GenAI, Meta | |
| L3: | |
| L4: # GAIA: A Benchmark for General AI Assistants | |
| L5: | |
| L6: Grégoire Mialon Clémentine Fourrier Craig Swift Thomas Wolf Yann LeCun Thomas | |
| L7: Scialom [ [ [ [ {gmialon,tscialom}@meta.com clementine@huggingface.co | |
| L8: | |
| L9: ###### Abstract | |
| L10: | |
| L11: We introduce GAIA, a benchmark for General AI Assistants that, if solved, would | |
| L12: represent a milestone in AI research. GAIA proposes real-world questions that | |
| L13: require a set of fundamental abilities such as reasoning, multi-modality | |
| L14: handling, web browsing, and generally tool-use proficiency. GAIA questions are | |
| L15: conceptually simple for humans yet challenging for most advanced AIs: we show | |
| L16: that human respondents obtain 92% vs. 15% for GPT-4 equipped with plugins. This | |
| L17: notable performance disparity contrasts with the recent trend of LLMs | |
| L18: outperforming humans on tasks requiring professional skills in e.g. law or | |
| L19: chemistry. GAIA’s philosophy departs from the current trend in AI benchmarks | |
| L20: suggesting to target tasks that are ever more difficult for humans. We posit | |
| L21: that the advent of Artificial General Intelligence (AGI) hinges on a system’s | |
| L22: capability to exhibit similar robustness as the average human does on such | |
| L23: questions. Using GAIA’s methodology, we devise 466 questions and their answer. | |
| L24: We release our questions while retaining answers to 300 of them to power a | |
| L25: leader-board hereby accessible. | |
| L26: | |
| L27: \correspondence | |
| L28: | |
| L29: ## 1 Introduction | |
| L30: | |
| L31: Large Language Models (LLMs) arguably open the way to general purpose systems. | |
| L32: Indeed, the latest among them (OpenAI, 2023; Anthropic, 2023; Anil et al., 2023; | |
| L33: Touvron et al., 2023) are fluent, knowledgeable, aligned to some extent with | |
| L34: human preferences (Ouyang et al., 2022), and can be augmented (Mialon et al., | |
| L35: 2023) with tools such as web browsers or code interpreters in a zero or few-shot | |
| L36: setting (Brown et al., 2020). However, evaluating these systems is an open | |
| L37: problem: given their emerging new capabilities, LLMs are regularly breaking AI | |
| L38: benchmarks, at an ever-increasing rate (Kiela et al., 2023). | |
| L39: | |
| L40: In search for more challenging benchmarks, current trend suggests to seek tasks | |
| L41: that are ever more difficult for humans, and challenge LLMs with more intricate | |
| L42: educational assessments, for example in STEM and Law, or target more complex | |
| L43: realisations, such as writing a coherent book. But, tasks that are difficult for | |
| L44: humans are not necessarily difficult for recent systems: the challenging MMLU | |
| L45: or GSM8k benchmarks for example (Hendrycks et al., 2021; Cobbe et al., 2021) are | |
| L46: already close to be solved,111GPT4 does 86.4% on MMLU. Human non-specialist | |
| L47: accuracy on the benchmark is only 34.5% Expert-level human performance is | |
| L48: estimated at 89.8%. due to rapid LLM improvement possibly combined with data | |
| L49: contamination.222See for example the case of Hellaswag. Furthermore, open-ended | |
| L50: generation generally requires human or model-based evaluation (Zheng et al., | |
| L51: 2023). Human evaluation will become less and less feasible when increasing the | |
| L52: task complexity, e.g. in terms of output length or required skills: how to | |
| L53: evaluate a book generated by an AI, or solutions to maths problems that few | |
| L54: people in the world can solve? Model-based evaluations on the other hand are by | |
| L55: construction dependent of stronger models hence cannot evaluate new state-of- | |
| L56: the-art models, without mentioning potential subtle biases such as preferring | |
| L57: the first choice presented (Zheng et al., 2023). Overall, evaluating new AI | |
| L58: systems requires to rethink benchmarks (Chollet, 2019). | |
| L59: | |
| L60: Alternatively to tasks that are harder for humans, AI systems could be asked to | |
| L61: solve conceptually simple tasks yet that require accurate execution of complex | |
| L62: sequences of actions, with large combinatorial spaces. The output could only be | |
| L63: obtained upon successful completion of the task and be easy to validate, | |
| L64: analogous to the Proof of Work algorithm (Jakobsson and Juels, 1999; Dwork and | |
| L65: Naor, 1993), where a computer is asked to solve a complex problem whose solution | |
| L66: is easy to verify. Tasks for AI assistants, given their need for access to a | |
| L67: diverse and uncertain world, meet this criterion while being inherently rooted | |
| L68: in practical use cases. | |
| L69: | |
| L70: We move in that direction by proposing GAIA, a benchmark for General AI | |
| L71: Assistants featuring 466 carefully crafted questions and their answer, along | |
| L72: with the associated design methodology. Our questions are easy to create, | |
| L73: challenging for AI systems—for LLMs, most require complex generations—, yet | |
| L74: admit a unique, factual answer, allowing a simple and robust automatic | |
| L75: evaluation. | |
| L76: | |
| L77: GAIA attempts to avoid current pitfalls of LLMs evaluation by targeting: | |
| L78: | |
| L79: Real-world and challenging questions. For example, a LLM will typically need to | |
| L80: browse the open and changing web, handle multi-modality, or reason over multiple | |
| L81: steps to answer our questions. Conversely, many LLM benchmarks are quite | |
| L82: specific and/or restricted to closed and synthetic environments. | |
| L83: | |
| L84: Easy interpretability through conceptually simple tasks—non experts annotators | |
| L85: exhibit a near perfect score—, associated reasoning trace, and few but highly | |
| L86: curated questions. This is in contrast with aggregated benchmarks that can lack | |
| L87: efficiency and reliability (Perlitz et al., 2023). | |
| L88: | |
| L89: Non-gameability. Answering the questions requires successful completion of some | |
| L90: number of steps, which cannot easily be brute forced due to their diversity. The | |
| L91: possibility to check the reasoning trace, the accuracy required in the answers, | |
| L92: their absence in plain text from the internet prevent a possible data | |
| L93: contamination. In contrast, multiple choice answers (e.g., MMLU) make | |
| L94: contamination assessment more difficult since a wrong reasoning trace can more | |
| L95: easily get to the correct choice. | |
| L96: | |
| L97: Simplicity of use. Crucially, the answers to our questions are factoid, concise | |
| L98: and unambiguous. These properties allow simple, fast and factual evaluation. Our | |
| L99: questions are meant to be answered in zero shot, limiting the influence of the | |
| L100: evaluation setup. By opposition, many LLM benchmarks require evaluations that | |
| L101: are sensitive to the experimental setup such as the number and nature of prompts | |
| L102: (Liang et al., 2022b) (Section 8.2), or the benchmark | |
| L103: implementation.333https://huggingface.co/blog/evaluating-mmlu-leaderboard | |
| L104: | |
| L105: In spite of being successful at tasks that are difficult for humans, the most | |
| L106: capable LLMs do poorly on GAIA. Even equipped with tools, GPT4 does not exceed a | |
| L107: 30% success rate for the easiest of our tasks, and 0% for the hardest. In the | |
| L108: meantime, the average success rate for human respondents is 92%. Consequently, a | |
| L109: system capable of solving GAIA can be assessed in the context of t-AGI,444As | |
| L110: defined in https://www.alignmentforum.org/posts/BoA3agdkAzL6HQtQP/clarifying- | |
| L111: and-predicting-agi, a t-AGI beats, on most tasks, most human experts who are | |
| L112: given time t to perform the task noting that humans typically take between 6 | |
| L113: minutes for the simplest questions to 17 minutes for the most complex ones. From | |
| L114: a related perspective, such system would arguably be a competent General AI | |
| L115: within the framework recently proposed in Morris et al. (2023), which also | |
| L116: appear to be the next milestone in AI research since ChatGPT (OpenAI, 2023) is | |
| L117: one level below. This paper covers the composition of GAIA, its design choices, | |
| L118: and explain how to craft questions and the associated challenges so that the </result> | |
| <result>Error fetching URL `https://github.com/sambanova/toolbench`</result> | |
| <result>Error fetching URL `https://hotpotqa.github.io/`</result> | |
| <result>[27] (https://aclanthology.org/N18-1074.pdf) | |
| **viewing lines [0 - 134] of 809** | |
| L0: | |
| L1: URL: https://aclanthology.org/N18-1074.pdf | |
| L2: ===== Page 1 ===== | |
| L3: | |
| L4: # FEVER: a large-scale dataset for Fact Extraction and VERification | |
| L5: | |
| L6: James Thorne\(^1\), Andreas Vlachos\(^1\), Christos Christodoulopoulos\(^2\), | |
| L7: and Arpit Mittal\(^2\) | |
| L8: | |
| L9: \(^1\)Department of Computer Science, University of Sheffield | |
| L10: \(^2\)Amazon Research Cambridge | |
| L11: {j.thorne, a.vlachos}@sheffield.ac.uk | |
| L12: {chrchrs, mitarpit}@amazon.co.uk | |
| L13: | |
| L14: ## Abstract | |
| L15: | |
| L16: In this paper we introduce a new publicly available dataset for verification | |
| L17: against textual sources, FEVER: Fact Extraction and VERification. It consists of | |
| L18: 185,445 claims generated by altering sentences extracted from Wikipedia and | |
| L19: subsequently verified without knowledge of the sentence they were derived from. | |
| L20: The claims are classified as Supported, Refuted or NotEnoughInfo by annotators | |
| L21: achieving 0.6841 in Fleiss \(\kappa\). For the first two classes, the annotators | |
| L22: also recorded the sentence(s) forming the necessary evidence for their | |
| L23: judgment. To characterize the challenge of the dataset presented, we develop a | |
| L24: pipeline approach and compare it to suitably designed oracles. The best accuracy | |
| L25: we achieve on labeling a claim accompanied by the correct evidence is 31.87%, | |
| L26: while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is | |
| L27: a challenging testbed that will help stimulate progress on claim verification | |
| L28: against textual sources. | |
| L29: | |
| L30: ## 1 Introduction | |
| L31: | |
| L32: The ever-increasing amounts of textual information available combined with the | |
| L33: ease in sharing it through the web has increased the demand for verification, | |
| L34: also referred to as fact checking. While it has received a lot of attention in | |
| L35: the context of journalism, verification is important for other domains, e.g. | |
| L36: information in scientific publications, product reviews, etc. | |
| L37: | |
| L38: In this paper we focus on verification of textual claims against textual | |
| L39: sources. When compared to textual entailment (TE)/natural language inference | |
| L40: (Dagan et al., 2009; Bowman et al., 2015), the key difference is that in these | |
| L41: tasks the passage to verify each claim is given, and in recent years it | |
| L42: typically consists a single sentence, while in verification systems it is | |
| L43: retrieved from a large set of documents in order to form the evidence. Another | |
| L44: related task is question answering (QA), for which approaches have recently been | |
| L45: extended to handle large-scale resources such as Wikipedia (Chen et al., 2017). | |
| L46: However, questions typically provide the information needed to identify the | |
| L47: answer, while information missing from a claim can often be crucial in | |
| L48: retrieving refuting evidence. For example, a claim stating "Fiji's largest | |
| L49: island is Kauai." can be refuted by retrieving "Kauai is the oldest Hawaiian | |
| L50: Island." as evidence. | |
| L51: | |
| L52: Progress on the aforementioned tasks has benefited from the availability of | |
| L53: large-scale datasets (Bowman et al., 2015; Rajpurkar et al., 2016). However, | |
| L54: despite the rising interest in verification and fact checking among researchers, | |
| L55: the datasets currently used for this task are limited to a few hundred claims. | |
| L56: Indicatively, the recently conducted Fake News Challenge (Pomerleau and Rao, | |
| L57: 2017) with 50 participating teams used a dataset consisting of 300 claims | |
| L58: verified against 2,595 associated news articles which is orders of magnitude | |
| L59: smaller than those used for TE and QA. | |
| L60: | |
| L61: In this paper we present a new dataset for claim verification, FEVER: Fact | |
| L62: Extraction and VERification. It consists of 185,445 claims manually verified | |
| L63: against the introductory sections of Wikipedia pages and classified as | |
| L64: Supported, Refuted or NotEnoughInfo. For the first two classes, systems and | |
| L65: annotators need to also return the combination of sentences forming the | |
| L66: necessary evidence supporting or refuting the claim (see Figure 1). The claims | |
| L67: were generated by human annotators extracting claims from Wikipedia and mutating | |
| L68: them in a variety of ways, some of which were meaning-altering. The | |
| L69: verification of each | |
| L70: | |
| L71: 809 | |
| L72: | |
| L73: Proceedings of NAACL-HLT 2018, pages 809–819 | |
| L74: | |
| L75: New Orleans, Louisiana, June 1 - 6, 2018. ©2018 Association for Computational | |
| L76: Linguistics | |
| L77: | |
| L78: ===== Page 2 ===== | |
| L79: | |
| L80: claim was conducted in a separate annotation process by annotators who were | |
| L81: aware of the page but not the sentence from which original claim was extracted | |
| L82: and thus in 31.75% of the claims more than one sentence was considered | |
| L83: appropriate evidence. Claims require composition of evidence from multiple | |
| L84: sentences in 16.82% of cases. Furthermore, in 12.15% of the claims, this | |
| L85: evidence was taken from multiple pages. | |
| L86: | |
| L87: To ensure annotation consistency, we developed suitable guidelines and user | |
| L88: interfaces, resulting in inter-annotator agreement of 0.6841 in Fleiss (Fleiss, | |
| L89: 1971) in claim verification classification, and 95.42% precision and 72.36% | |
| L90: recall in evidence retrieval. | |
| L91: | |
| L92: To characterize the challenges posed by FEVER we develop a pipeline approach | |
| L93: which, given a claim, first identifies relevant documents, then selects | |
| L94: sentences forming the evidence from the documents and finally classifies the | |
| L95: claim w.r.t. evidence. The best performing version achieves 31.87% accuracy in | |
| L96: verification when requiring correct evidence to be retrieved for claims | |
| L97: Supported or Refuted, and 50.91% if the correctness of the evidence is ignored, | |
| L98: both indicating the difficulty but also the feasibility of the task. We also | |
| L99: conducted oracle experiments in which components of the pipeline were replaced | |
| L100: by the gold standard annotations, and observed that the most challenging part of | |
| L101: the task is selecting the sentences containing the evidence. In addition to | |
| L102: publishing the data via our website1, we also publish the annotation interfaces2 | |
| L103: and the baseline system3 to stimulate further research on verification. | |
| L104: | |
| L105: Footnote 1: http://fever.ai | |
| L106: | |
| L107: Footnote 2: https://github.com/awslabs/fever | |
| L108: | |
| L109: Footnote 3: https://github.com/sheffieldnlp/fever-baselines | |
| L110: | |
| L111: ## 2 Related Works | |
| L112: | |
| L113: Vlachos and Riedel (2014) constructed a dataset for claim verification | |
| L114: consisting of 106 claims, selecting data from fact-checking websites such as | |
| L115: PolitiFact, taking advantage of the labelled claims available there. However, in | |
| L116: order to develop claim verification components we typically require the | |
| L117: justification for each verdict, including the sources used. While this | |
| L118: information is usually available in justifications provided by the journalists, | |
| L119: they are not in a machine-readable form. Thus, also considering the small number | |
| L120: of claims, the task defined by the dataset proposed remains too challenging for | |
| L121: the ML/NLP methods currently available. Wang (2017) extended this approach by | |
| L122: including all 12.8K claims available by Politifact via its API, however the | |
| L123: justification and the evidence contained in it was ignored in the experiments as | |
| L124: it was not machine-readable. Instead, the claims were classified considering | |
| L125: only the text and the metadata related to the person making the claim. While | |
| L126: this rendered the task amenable to current NLP/ML methods, it does not allow for | |
| L127: verification against any sources and no evidence needs to be returned to | |
| L128: justify the verdicts. | |
| L129: | |
| L130: The Fake News challenge (Pomerleau and Rao, 2017) modelled verification as | |
| L131: stance classification: given a claim and an article, predict whether the article | |
| L132: supports, refutes, observes (neutrally states the claim) or is irrelevant to | |
| L133: the claim. It consists of 50K labelled claim-article pairs, combining 300 claims | |
| L134: with 2,582 articles. The claims and the articles were curated and labeled by </result> | |
| <result>[28] (https://nlp.cs.washington.edu/triviaqa/) | |
| **viewing lines [0 - 48] of 48** | |
| L0: | |
| L1: URL: https://nlp.cs.washington.edu/triviaqa/ | |
| L2: ## TriviaQA: A Large Scale Dataset for Reading Comprehension and Question | |
| L3: Answering | |
| L4: | |
| L5: TriviaQA is a reading comprehension dataset containing over 650K question- | |
| L6: answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by | |
| L7: trivia enthusiasts and independently gathered evidence documents, six per | |
| L8: question on average, that provide high quality distant supervision for answering | |
| L9: the questions. The details can be found in our ACL 17 paper TriviaQA: A Large | |
| L10: Scale Distantly Supervised Challenge Dataset for Reading Comprehension | |
| L11: | |
| L12: Mandar Joshi, Eunsol Choi, Daniel Weld, Luke Zettlemoyer. TriviaQA: A Large | |
| L13: Scale Distantly Supervised Challenge Dataset for Reading Comprehension | |
| L14: | |
| L15: In Association for Computational Linguistics (ACL) 2017, Vancouver, Canada. | |
| L16: [bib] | |
| L17: | |
| L18: ## News | |
| L19: | |
| L20: ##### Jul 2017 | |
| L21: | |
| L22: The TriviaQA leaderboard is now live on Codalab. Submit your predictions for | |
| L23: evaluation on the test set! | |
| L24: | |
| L25: ## Data | |
| L26: | |
| L27: If you are interested in the reading comprehension task motivated in the paper, | |
| L28: click on the link below to download the data. | |
| L29: | |
| L30: If you are interested in open domain QA, click on the link below to download the | |
| L31: data. It contains the unfiltered dataset with 110K question-answer pairs. The | |
| L32: Wikipedia and top 10 search documents can be obtained from the RC version. The | |
| L33: main difference between the RC version above and the unfiltered dataset is that | |
| L34: not all documents (in the unfiltered set) for a given question contain the | |
| L35: answer string(s). This makes the unfiltered dataset more appropriate for IR- | |
| L36: style QA. | |
| L37: | |
| L38: The University of Washington does not own the copyright of the questions and | |
| L39: documents included in TriviaQA. | |
| L40: | |
| L41: ## Code | |
| L42: | |
| L43: Check out our Github repository. | |
| L44: | |
| L45: ## Contact | |
| L46: | |
| L47: For any questions about the code or data, please contact Mandar Joshi -- {first | |
| L48: name of the first author}90[at]cs[dot]washington[dot]edu</result> | |
| <result>Error fetching URL `https://research.google/pubs/natural-questions-a-benchmark-for-question-answering-research/`</result> | |
| <result>Error fetching URL `https://microsoft.github.io/msmarco/`</result> | |
| <result>Error fetching URL `https://github.com/beir-cellar/beir`</result> | |
| <result>Error fetching URL `https://project-miracl.github.io/`</result> | |
| <result>[29] (https://ar5iv.org/abs/2207.01206) | |
| **viewing lines [0 - 122] of 1392** | |
| L0: | |
| L1: URL: https://ar5iv.org/abs/2207.01206 | |
| L2: # WebShop: Towards Scalable Real-World Web Interaction with Grounded Language | |
| L3: Agents | |
| L4: | |
| L5: Shunyu Yao111Equal contribution. Project site with code, data, and demos: | |
| L6: https://webshop-pnlp.github.io. Howard Chen111Equal contribution. Project site | |
| L7: with code, data, and demos: https://webshop-pnlp.github.io. John Yang Karthik | |
| L8: Narasimhan | |
| L9: | |
| L10: Department of Computer Science, Princeton University | |
| L11: {shunyuy, howardchen, jy1682, karthikn}@princeton.edu | |
| L12: | |
| L13: ###### Abstract | |
| L14: | |
| L15: Existing benchmarks for grounding language in interactive environments either | |
| L16: lack real-world linguistic elements, or prove difficult to scale up due to | |
| L17: substantial human involvement in the collection of data or feedback signals. To | |
| L18: bridge this gap, we develop WebShop – a simulated e-commerce website environment | |
| L19: with million real-world products and 1.181.18 crowd-sourced text instructions. | |
| L20: Given a text instruction specifying a product requirement, an agent needs to | |
| L21: navigate multiple types of webpages and issue diverse actions to find, | |
| L22: customize, and purchase an item. WebShop provides several challenges for | |
| L23: language grounding including understanding compositional instructions, query | |
| L24: (re-)formulation, comprehending and acting on noisy text in webpages, and | |
| L25: performing strategic exploration. We collect over 12,08712,087 human | |
| L26: demonstrations for the task, and train and evaluate a diverse range of agents | |
| L27: using reinforcement learning, imitation learning, and pre-trained image and | |
| L28: language models. Our best model achieves a task success rate of 1,6001,600, | |
| L29: which outperforms rule-based heuristics (29%29\%) but is far lower than human | |
| L30: expert performance (9.6%9.6\%). We also analyze agent and human trajectories and | |
| L31: ablate various model components to provide insights for developing future | |
| L32: agents with stronger language understanding and decision making abilities. | |
| L33: Finally, we show that agents trained on WebShop exhibit non-trivial sim-to-real | |
| L34: transfer when evaluated on amazon.com and ebay.com , indicating the potential | |
| L35: value of WebShop in developing practical web-based agents that can operate in | |
| L36: the wild.59%59\% | |
| L37: | |
| L38: ## 1 Introduction | |
| L39: | |
| L40: Recent advances in natural language processing (NLP) and reinforcement learning | |
| L41: (RL) have brought about several exciting developments in agents that can perform | |
| L42: sequential decision making while making use of linguistic context [30, 50, 58]. | |
| L43: On the other hand, large-scale language models like GPT-3 [6] and BERT [11] are | |
| L44: excelling at traditional NLP benchmarks such as text classification, | |
| L45: information extraction and question answering. While the former set of tasks are | |
| L46: limited in their set of linguistic concepts and prove difficult to scale up, | |
| L47: the latter tasks usually contain static, non-interactive datasets that lack | |
| L48: adequate grounding to extra-linguistic concepts [4]. In order to make further | |
| L49: progress in building grounded language models, we believe there is a need for | |
| L50: scalable interactive environments that contain: (1) language elements that | |
| L51: reflect rich, real-world usage and are collectible at scale, and (2) task | |
| L52: feedback that is well-defined and automatically computable to facilitate | |
| L53: interactive learning, without the constant need for expensive feedback from | |
| L54: humans. | |
| L55: | |
| L56: The world wide web (WWW) is a massive open-domain interactive environment that | |
| L57: inherently satisfies the first aforementioned requirement through its | |
| L58: interconnected set of pages with natural text, images and interactive elements. | |
| L59: By being simultaneously scalable, semantic, interactive, dynamic and realistic, | |
| L60: the web is uniquely different from existing environments for autonomous agents | |
| L61: like games or 3D navigation. Moreover, the web also provides a practical | |
| L62: environment to deploy trained agents, with great potential for alleviating human | |
| L63: efforts in tedious tasks (e.g. buying products, booking appointments). While | |
| L64: there has been prior work on building web-based tasks, they either lack depth in | |
| L65: the transition and action spaces, or prove difficult to scale up. Some | |
| L66: benchmarks only contain either a single classification task [39, 46, 31] or | |
| L67: interactions containing only a handful of different pages in each episode [43]. | |
| L68: Others propose tasks with longer horizons but are either limited to following | |
| L69: hyperlinks for web navigation [36] or require human-in-the-loop feedback due to | |
| L70: the lack of an automated reward function [33]. | |
| L71: | |
| L72: In this paper, we introduce WebShop (Figure 1) – a large-scale interactive web- | |
| L73: based environment for language understanding and decision making – and train | |
| L74: autonomous agents to complete tasks on this benchmark. With the goals of being | |
| L75: scalable and containing realistic language and visual elements, WebShop emulates | |
| L76: the task of online shopping on an e-commerce website, where the agent’s goal is | |
| L77: to understand a human-provided text instruction and purchase a product to match | |
| L78: the specifications. To do so, the agent needs to query the website’s search | |
| L79: engine, choose items to explore from search results, open and read their | |
| L80: description and details, and select the necessary options (e.g. 32 oz., red | |
| L81: color) before clicking the ‘Buy’ button. In order to pick the optimal product | |
| L82: that matches user requirements, the agent may need to view and compare various | |
| L83: products (including backtracking between pages), and potentially perform | |
| L84: multiple searches. WebShop contains over one million products scraped from | |
| L85: amazon.com, over thousand crowdsourced instructions, and a diverse semantic | |
| L86: action space of searching text queries and choosing text buttons. It is packaged | |
| L87: into a convenient OpenAI Gym [5] environment and can be rendered in two modes | |
| L88: (HTML or simple) with parallel observation spaces that are easy for human and | |
| L89: model respectively. Rewards are automatically computed using a combination of | |
| L90: programmatic matching functions that consider the attributes, type, options and | |
| L91: price of the chosen product, alleviating the need for human evaluation and | |
| L92: providing a path to scaling up interactive learning.1212 | |
| L93: | |
| L94: We develop several agents to perform this task, using both reinforcement | |
| L95: learning (RL) and imitation learning (IL). We also leverage the latest pre- | |
| L96: trained language models [26, 11] for representing and generating text. Our | |
| L97: modular architecture includes a factorized processing of state observations and | |
| L98: action choices using ResNets (visual) and Transformers (text), followed by an | |
| L99: attention fusion layer that helps the agent contextually score each action. Our | |
| L100: best agent achieves an average score of (out of 62.462.4) and successfully | |
| L101: completes the task 100100 of the time, significantly higher than a heuristic | |
| L102: baseline that achieves 28.7%28.7\% and 45.645.6, respectively. While this | |
| L103: demonstrates the potential for IL and RL, the agents are still much lower than | |
| L104: human experts, who can achieve 9.6%9.6\% and 82.182.1 on this task.*** In our | |
| L105: analysis (§5.3), we observe that the task requires patience and consistency, | |
| L106: which is lacking in some crowdsource workers, leading to lower scores. Even with | |
| L107: this caveat, the gap between human performance and the model remains | |
| L108: significant. We perform several analyses and ablation studies to identify the | |
| L109: cause of this gap and find several avenues for agent improvement in the future | |
| L110: including more robust search generation, explicit memory modules, and better | |
| L111: handling of noisy web text. Finally, we also demonstrate an instance of sim-to- | |
| L112: real transfer by deploying agents trained with WebShop to operate on amazon.com | |
| L113: and ebay.com, and find that they can achieve similar performances despite search | |
| L114: engine and product differences, and consistently outperform the rule baseline | |
| L115: of using the first result returned by the commercial search engines when | |
| L116: directly searching the instruction texts. This demonstrates the practical | |
| L117: potential of our work towards developing agents that can operate autonomously on | |
| L118: the world wide web (WWW).59.6%59.6\% | |
| L119: | |
| L120: ## 2 Related Work | |
| L121: | |
| L122: Reinforcement learning on the web. Nogueira and Cho [36] introduced WikiNav as a</result> | |
| <result>Error fetching URL `http://alfworld.github.io/`</result> | |
| <result>Error fetching URL `https://osu-nlp-group.github.io/Mind2Web/`</result> | |
| <result>Error fetching URL `https://github.com/web-arena-x/visualwebarena`</result> | |
| <result>[30] (https://ar5iv.org/pdf/2406.12172) | |
| **viewing lines [0 - 127] of 1478** | |
| L0: | |
| L1: URL: https://ar5iv.org/pdf/2406.12172 | |
| L2: # Navigating the Labyrinth: Evaluating and Enhancing LLMs’ Ability to Reason | |
| L3: About Search Problems | |
| L4: | |
| L5: Nasim Borazjanizadeh | |
| L6: | |
| L7: Berkeley AI Research, UC Berkeley | |
| L8: \AndRoei Herzig | |
| L9: Berkeley AI Research, UC Berkeley | |
| L10: \AndTrevor Darrell | |
| L11: Berkeley AI Research, UC Berkeley | |
| L12: \AndRogerio Feris | |
| L13: MIT-IBM Watson AI Lab | |
| L14: \AndLeonid Karlinsky | |
| L15: MIT-IBM Watson AI Lab | |
| L16: | |
| L17: ###### Abstract | |
| L18: | |
| L19: Recently, Large Language Models (LLMs) attained impressive performance in math | |
| L20: and reasoning benchmarks. However, they still often struggle with logic problems | |
| L21: and puzzles that are relatively easy for humans. To further investigate this, | |
| L22: we introduce a new benchmark, SearchBench, containing 11 unique search problems, | |
| L23: each equipped with automated pipelines to generate an arbitrary number of | |
| L24: instances and analyze the feasibility, correctness, and optimality of LLM- | |
| L25: generated solutions. We show that even the most advanced LLMs fail to solve | |
| L26: these problems end-to-end in text, e.g., GPT4 solves only 1.4%. SearchBench | |
| L27: problems require considering multiple pathways to the solution as well as | |
| L28: backtracking, posing a significant challenge to auto-regressive models. | |
| L29: Instructing LLMs to generate code that solves the problem helps, but only | |
| L30: slightly, e.g., GPT4’s performance rises to 11.7%. In this work, we show that | |
| L31: in-context learning with A* algorithm implementations enhances performance. The | |
| L32: full potential of this promoting approach emerges when combined with our | |
| L33: proposed Multi-Stage-Multi-Try method, which breaks down the algorithm | |
| L34: implementation into two stages and verifies the first stage against unit tests, | |
| L35: raising GPT-4’s performance above 57%. | |
| L36: | |
| L37: \doparttoc\faketableofcontents | |
| L38: | |
| L39: ### 1 Introduction | |
| L40: | |
| L41: The advent of Large Language Models (LLMs) has revolutionized the field of | |
| L42: natural language processing, with models like Gemini[18], GPT-4[26] | |
| L43: demonstrating unprecedented performance on reasoning tasks such as GSM8k[8]. | |
| L44: However, these models still exhibit surprising failures on some intuitive | |
| L45: tasks[2, 30, 22] and struggle with multi-step compositional reasoning, | |
| L46: combinatorial problems, and planning [9, 40, 44]. Inspired by these observations | |
| L47: and to further investigate LLMs’ reasoning abilities, we offer a new benchmark | |
| L48: of search problems, SearchBench. The problems in SearchBench are combinatorial, | |
| L49: defined as tasks that involve finding an optimal object from a finite set of | |
| L50: objects, where the set of feasible solutions is either discrete or can be | |
| L51: reduced to a discrete set [43]. These problems are predominantly NP-hard and | |
| L52: necessitate systematic exploration of action paths and backtracking to | |
| L53: intermediate feasible states; thus, SearchBench implicitly investigates the | |
| L54: LLM’s capacity for non-linear reasoning. | |
| L55: | |
| L56: SearchBench has five distinct problem categories: (i) pathfinding, (ii) puzzles, | |
| L57: (iii) subset sum, (iv) sorting, and (v) under-determined systems; further | |
| L58: divided into 11 unique problem types. Each problem type is inspired by known | |
| L59: puzzles and combinatorial problems but augmented with modified rules and | |
| L60: constraints to ensure substantial differences from similar problems LLMs | |
| L61: encountered during their training. And the solution to each problem is a | |
| L62: sequence of actions leading from the initial state to the goal state, while | |
| L63: optimizing a cost. We generate100 instances of varying difficulty per problem | |
| L64: type using an automatic pipeline, resulting in 1107 problem instances total. | |
| L65: Each problem type in SearchBench is equipped with an automatic pipeline that | |
| L66: evaluates LLM-generated solutions on three dimensions: feasibility, correctness, | |
| L67: and optimality. Feasibility checks whether the actions taken follow the | |
| L68: problem’s rules; correctness verifies if a feasible solution reaches the goal | |
| L69: state; and optimality checks if the least cost solution was found.∼\sim | |
| L70: | |
| L71: SearchBench is challenging to LLMs due to several factors. Firstly, natural | |
| L72: language is less suited for describing or updating accurate representations of | |
| L73: complex intermediate states. Secondly, our experiments show LLMs struggle with | |
| L74: exploring a combinatorial exponentially exploding state-space. Despite the fact | |
| L75: that some methods were developed for long-context reasoning [4, 13, 50], | |
| L76: SearchBench problems cannot be easily summarized [4], reasoned about [13], or | |
| L77: processed in parallel due to their size [50, 45]. Our findings show that even | |
| L78: the strongest LLMs [26] almost completely fail to solve SearchBench problems in | |
| L79: text-only mode. | |
| L80: | |
| L81: To provide further insights, we show that LLMs’ performance on SearchBench | |
| L82: improves by prompting the models to solve the problems using the A* search | |
| L83: algorithm [11]. A* is a heuristic-based graph traversal algorithm known for its | |
| L84: time efficiency and provable optimality guarantees, making it the most suitable | |
| L85: search algorithm for solving the problems in our benchmark. This method | |
| L86: leverages A*’s correctness and optimality, while offloading some of the non- | |
| L87: linear computations involved in searching the state-space to code execution. | |
| L88: Additionally, to improve the quality of generated A* codes, motivated that | |
| L89: ensembling helps generation quality[41, 47, 21], we introduce the Multi-Stage- | |
| L90: Multi-Try (MSMT) inference strategy. In the "Multi-Try" aspect of MSMT, before | |
| L91: evaluating the solution returned by the code, we first verify whether the code | |
| L92: generated by the model satisfies a set of unit tests: (i) it is executable; (ii) | |
| L93: it returns a list as output; and (iii) data type of list elements is correct. | |
| L94: If the code fails any of the tests, MSMT re-runs the LLM until a valid code is | |
| L95: generated or allowed number of attempts is exhausted. The "Multi-Stage" aspect | |
| L96: of MSMT generates the code in two steps: (i) ‘A* Implementation’ - the | |
| L97: implementation of an instance-agnostic A* algorithm for the problem type; and | |
| L98: (ii) Initialization - the instantiation of initial conditions and state | |
| L99: variables of the problem instance. In MSMT ’Initialization’ is generated | |
| L100: conditioned on the ‘A* Implementation’ (which is generated separately first and | |
| L101: provided in ‘Intitialization’ prompt). We demonstrate that our MSMT A* method | |
| L102: (Fig. 2) significantly enhances the LLMs’ ability to solve search problems, | |
| L103: outperforming all other prompting strategies we used to evaluate models on | |
| L104: SearchBench, including 0-shot text, 4-shot Chain-of-Thought (CoT)[42] text, | |
| L105: 0-shot code generation, and 4-shot A* prompting with the naive greedy decoding | |
| L106: strategy. | |
| L107: | |
| L108: To summarize, our main contributions are as follows: (i) We contribute the | |
| L109: SearchBench benchmark designed to assess the capability of LLMs in solving | |
| L110: state-based problems requiring combinatorial search; (ii) We introduce the A* | |
| L111: Multi-Stage-Multi-Try (MSMT) framework that leverages the flexibility and | |
| L112: natural language comprehension capabilities of LLMs, reliability and structured | |
| L113: nature of the A* algorithm, along with unitests to verify LLMs’ code generation | |
| L114: in order to address search problems. (iii) We show a strong advantage of MSMT A* | |
| L115: prompting for solving search problems robustly with LLMs, suggesting | |
| L116: interesting future work directions and yet leaving significant room for future | |
| L117: improvement on SearchBench. | |
| L118: | |
| L119: ### 2 SearchBench Benchmark | |
| L120: | |
| L121: SearchBench includes five categories of problems: puzzles, subset sum, sorting, | |
| L122: pathfinding, and under-determined systems, further subdivided into 11 problem | |
| L123: types and instantiated as 1107 natural language problem instances. Each problem | |
| L124: type requires a different algorithmic approach to solve. Our problems are | |
| L125: inspired by well-known (mostly NP-hard) puzzles and search problems, though | |
| L126: their rules were modified to ensure a solved instance of a problem similar to | |
| L127: any SearchBench problem was not observed during LLMs’ massive internet-scale </result> | |
| <result>Error fetching URL `https://ar5iv.labs.ar5iv.org/html/2312.15820`</result> | |
| <result>Error fetching URL `https://github.com/jxwuyi/WebNav`</result> | |
| </function_results> | |
| <think>在同时打开多个基准测试相关页面后，我们获得了大量信息。WebVoyager 是一个多模态网页智能体基准测试，专注于在真实网站上进行端到端任务执行，其特点包括多模态输入（屏幕截图和文本）、在15个常用网站上的300个任务，以及使用GPT-4V进行自动评估。使用场景是评估网页智能体在真实世界中的导航和任务完成能力。例题可能是在亚马逊上找到特定产品并将其加入购物车。 | |
| WebArena 是一个真实且可复现的网页环境，用于评估自主智能体。其特点包括四个功能完整的网站（电子商务、论坛、协作开发、内容管理）、812项长视野任务，以及专注于功能正确性的评估。使用场景是评估智能体在复杂网页任务中的表现。例题可能是在电子商务网站上购买符合特定标准的商品。 | |
| GAIA 是一个针对通用AI助手的基准测试，要求具备推理、多模态处理、网页浏览和工具使用等能力。其特点包括466个问题，对人类来说概念简单但对AI具有挑战性，答案简洁明确，支持自动评估。使用场景是评估AI助手在真实世界问题中的表现。例题可能是“找出2022年法国总统选举中获胜者的出生城市”。 | |
| ToolBench 是一个用于评估LLM工具操作能力的基准测试。其特点包括多样化的软件工具、真实世界任务，以及易于使用的基础设施。使用场景是评估智能体使用工具完成任务的能力。例题可能是使用计算器API计算表达式，或使用搜索API查找信息。 | |
| HotpotQA 是一个多跳问答基准测试，其特点包括基于Wikipedia的113k个问题-答案对，需要多文档推理，并提供句子级别的支持事实。使用场景是评估模型在回答需要多跳推理的问题时的能力。例题可能是“《了不起的盖茨比》的作者还写过哪些小说？”。 | |
| FEVER 是一个事实提取和验证基准测试，其特点包括185,445个基于Wikipedia的声明，需要分类为支持、反驳或信息不足，并需要提供证据句子。使用场景是评估系统验证声称事实的能力。例题可能是“斐济最大的岛屿是考艾岛”，系统需要检索证据来支持或反驳这一说法。 | |
| TriviaQA 是一个阅读理解基准测试，其特点包括超过65万个问题-答案-证据三元组，问题由琐事爱好者编写，并附带证据文档。使用场景是评估模型在开放域问答中的能力。例题可能是“哪位演员在《星球大战：原力觉醒》中扮演凯洛·伦？”。 | |
| Natural Questions 是一个问答基准测试，其特点包括来自谷歌搜索的真实问题，答案来自Wikipedia，并包含长答案和短答案。使用场景是评估开放域问答系统。例题可能是“谁写了《傲慢与偏见》？”，答案可能是“简·奥斯汀”。 | |
| MS MARCO 是一个用于深度学习搜索的基准测试，其特点包括来自Bing的真实搜索查询、人工生成的答案，以及用于段落排序和文档排序的任务。使用场景是训练和评估检索模型。例题可能是“如何冲泡咖啡？”，系统需要检索相关段落来回答。 | |
| BEIR 是一个用于零样本评估的异构信息检索基准测试，其特点包括18个数据集、9项任务，涵盖事实核查、问答、对话检索等。使用场景是评估检索模型在零样本设置下的泛化能力。例题可能是给定一个查询，在文档集合中检索相关文档。 | |
| MIRACL 是一个多语言信息检索基准测试，其特点涵盖18种语言，专注于ad-hoc检索，并包含人工标注的相关性判断。使用场景是评估多语言检索模型。例题可能是用中文查询“全球变暖的原因”，并检索相关文档。 | |
| WebShop 是一个模拟电子商务网站环境，用于评估语言智能体。其特点包括118万个真实产品、12,087条众包文本指令，以及需要导航、搜索和购买的任务。使用场景是评估智能体在电子商务任务中的表现。例题可能是“找到一款价格低于50美元的无线蓝牙耳机”。 | |
| ALFWorld 是一个将文本和具身环境对齐的基准测试，用于在文本世界中训练智能体，然后将其转移到具身环境中。其特点包括基于文本的环境，与ALFRED数据集并行，并专注于指令跟随。使用场景是评估智能体在家庭任务中的规划能力。例题可能是“在厨房里找到一个杯子，把它拿到客厅”。 | |
| Mind2Web 是一个用于评估网页通用智能体的基准测试，其特点包括来自真实网站的任务、需要多步交互，并专注于语言指令跟随。使用场景是评估智能体在复杂网页任务中的表现。例题可能是“在GitHub上创建一个新的仓库，命名为‘test-repo’”。 | |
| VisualWebArena 是一个用于多模态智能体的基准测试，其特点包括真实视觉网页任务、需要理解视觉和文本元素，并评估功能正确性。使用场景是评估多模态智能体在视觉接地网页任务中的表现。例题可能是“在纽约时报网站上找到今天头条新闻的图片”。 | |
| SearchBench 是一个用于评估LLM解决搜索问题能力的基准测试，其特点包括11种搜索问题类型、自动生成实例，并评估可行性、正确性和最优性。使用场景是评估LLM在组合搜索问题中的推理能力。例题可能是“在一个迷宫中找到从起点到终点的最短路径”。 | |
| WebVLN 是一个网站上的视觉语言导航基准测试，其特点包括基于问题的指令、需要导航网站，并评估导航成功率。使用场景是评估智能体在网站中基于视觉和语言指令进行导航的能力。例题可能是“找到‘联系我们’页面并提取电子邮件地址”。 | |
| WebNav 是一个用于目标驱动网页导航的基准测试，其特点包括部分观察环境、需要规划和理解自然语言指令。使用场景是评估智能体在网页导航中的能力。例题可能是“导航到维基百科上关于‘人工智能’的页面”。 | |
| 现在，我需要整理一份针对搜索智能体的基准测试列表。考虑到用户可能更关注中文资料，我会优先考虑那些有中文描述的基准测试，例如BrowseComp、HSCodeComp、GAIA、WebVoyager、WebArena、Mind2Web、WebShop、ALFWorld、HotpotQA、FEVER、TriviaQA、Natural Questions、MS MARCO、BEIR、MIRACL、SearchBench、WebVLN、WebNav。我会为每个基准测试提供特点、使用场景和例题。同时，我会引用相关来源，例如HSCodeComp论文、BrowseComp博客文章、WebVoyager论文、WebArena论文、GAIA论文、WebShop论文、SearchBench论文等。我的回答将用中文呈现，并确保内容全面、结构清晰。现在开始撰写回答。</think>针对搜索智能体（Search Agent）的评估，已涌现出多个侧重点不同的基准测试。以下将分类介绍这些基准，涵盖其核心特点、典型使用场景及例题。 | |
| --- | |
| ## 🔍 1. 网页浏览/导航类基准 | |
| 这类基准要求智能体在真实或模拟的网页环境中执行多步操作，以完成复杂任务。 | |
| | 基准 | 核心特点 | 使用场景 | 例题 | | |
| |------|----------|----------|------| | |
| | **BrowseComp**（OpenAI）【3†L16-L35】 | - 1,266 个挑战性问题，需持续导航、查找难以直接搜索的信息<br>- 问题设计保证人类在 10 分钟内无法解决，且现有模型（包括带浏览的 ChatGPT）也难以完成<br>- 答案为短字符串，便于自动验证 | 评估智能体在复杂网络浏览任务中的持久性、创造性搜索能力 | “找出 2018‑2023 年间在 EMNLP 会议上发表、第一作者本科毕业于达特茅斯学院、第四作者本科毕业于宾夕法尼亚大学的科学论文标题”【3†L45-L49】 | | |
| | **WebVoyager**【24†L18-L33】 | - 多模态（视觉+文本）端到端网页智能体基准<br>- 包含 300 个真实任务，覆盖 15 个常用网站（如 Amazon、Wikipedia）<br>- 使用 GPT‑4V 进行自动评估，与人工评估一致性达 85.3% | 评估智能体在真实网站上的端到端任务完成能力（多模态输入） | “在 Amazon 上找到‘无线蓝牙耳机’并按价格从低到高排序，然后选择最便宜的一款加入购物车” | | |
| | **WebArena**【25†L15-L35】 | - 完全可交互的、高度真实的网页环境（4 个领域：电商、论坛、协作开发、内容管理）<br>- 812 项长视野任务，评估功能正确性而非表面动作匹配<br>- 最佳 GPT‑4 智能体成功率仅 14.41%，远低于人类的 78.24% | 评估自主智能体在真实网页环境中的复杂任务执行能力 | “在电商网站中购买一双‘黑色运动鞋’，价格低于 $80，并将收货地址修改为纽约”【25†L86-L92】 | | |
| | **Mind2Web**【19†L4-L14】 | - 首个面向通用网页智能体的数据集，任务来自真实网站<br>- 支持在线评估（Online‑Mind2Web），包含 300 个实时任务、136 个流行网站<br>- 强调多步指令跟随与复杂交互 | 评估智能体在任意网站上的通用任务完成能力 | “在 GitHub 上创建一个名为 ‘test‑repo’ 的公开仓库，并添加一个 README.md 文件” | | |
| | **VisualWebArena**【20†L4-L10】 | - 多模态网页智能体基准，任务依赖视觉信息（截图）<br>- 包含丰富的视觉接地任务，如识别图片、按钮位置等<br>- 评估智能体在真实视觉网页环境中的表现 | 评估多模态智能体在视觉丰富的网页任务中的能力 | “在纽约时报网站找到今天头条新闻的配图，并描述图中主要物体” | | |
| | **WebVLN**【22†L4-L11】 | - 网站上的视觉‑语言导航任务，智能体需根据问题指令在网站中导航<br>- 数据集 WebVLN‑v1 包含多种网站导航场景<br>- 评估导航成功率与任务完成度 | 评估智能体在网站中基于视觉与语言指令的导航能力 | “找到‘联系我们’页面，并提取其中的邮箱地址” | | |
| | **WebNav**【23†L4-L11】 | - 目标驱动的网页导航基准，环境为部分观测<br>- 智能体需理解自然语言指令并在网页中规划动作序列<br>- 早期经典任务，常用于测试基础导航能力 | 评估智能体在部分观测网页环境中的规划与指令跟随能力 | “导航到 Wikipedia 上关于‘人工智能’的页面，并摘录第一段内容” | | |
| --- | |
| ## 🛠️ 2. 工具使用/通用助手类基准 | |
| 这类基准测试智能体调用外部工具（如搜索、计算、API）来解决实际问题的能力。 | |
| | 基准 | 核心特点 | 使用场景 | 例题 | | |
| |------|----------|----------|------| | |
| | **GAIA**【26†L10-L25】 | - 466 个真实世界问题，需要推理、多模态处理、网页浏览、工具使用等综合能力<br>- 对人类简单（92% 准确率），但对最强 GPT‑4 仅 15%<br>- 答案唯一、事实性，支持自动评估 | 评估通用 AI 助手在需要多步工具使用的真实任务中的表现 | “2022 年法国总统选举的获胜者出生在哪个城市？”【26†L10-L25】 | | |
| | **ToolBench**（OpenBMB）【7†L4-L10】 | - 包含多样软件工具的真实任务基准<br>- 提供易用的基础设施（虚拟 API 服务器等）<br>- 评估 LLM 调用工具完成复杂任务的能力 | 评估智能体在工具调用与组合上的熟练度 | “使用计算器 API 计算 (3.14 * 7.2)^2，然后使用搜索 API 查找‘圆周率’的最新精确值” | | |
| | **HSCodeComp**【2†L16-L35】 | - 首个针对深度搜索智能体在分层规则应用上的专家级电商基准<br>- 任务：根据嘈杂的商品描述预测 10 位 HS 编码（关税规则）<br>- 632 个商品条目，标注由多位电商专家完成，最佳智能体准确率仅 46.8%（人类 95%） | 评估智能体在复杂规则（如关税分类）下的深层推理与搜索能力 | “给定商品描述‘男士纯棉针织T恤，领口为V领’，预测其 10 位 HS 编码”【2†L24-L32】 | | |
| --- | |
| ## ❓ 3. 问答/检索类基准 | |
| 这类基准主要评估智能体在开放域或特定领域的信息检索与问答能力。 | |
| | 基准 | 核心特点 | 使用场景 | 例题 | | |
| |------|----------|----------|------| | |
| | **HotpotQA**【8†L4-L10】 | - 113k 个基于 Wikipedia 的多跳问答对<br>- 需要跨多个文档推理，并提供句子级支持事实<br>- 涵盖多样的问题类型（比较、列举、因果等） | 评估模型在需要多跳推理的开放域问答中的能力 | “《了不起的盖茨比》的作者还写过哪些小说？” | | |
| | **FEVER**【27†L16-L28】 | - 185,445 个基于 Wikipedia 的声明，需分类为 Supported/Refuted/NotEnoughInfo<br>- 要求提供证据句子（可多句、多页面）<br>- 挑战性高（最佳系统仅 31.87% 准确率） | 评估系统在事实核查与证据检索上的能力 | “斐济最大的岛屿是考艾岛。”【27†L48-L50】 | | |
| | **TriviaQA**【28†L5-L10】 | - 超过 650k 个问答‑证据三元组，问题由琐事爱好者编写<br>- 每个问题平均提供 6 篇证据文档，适合远程监督阅读<br>- 包含阅读理解和开放域 QA 两种任务设置 | 评估模型在开放域琐事问答中的检索与阅读理解能力 | “哪位演员在《星球大战：原力觉醒》中扮演凯洛·伦？” | | |
| | **Natural Questions**【11†L4-L8】 | - 来自 Google 搜索的真实用户问题，答案来自 Wikipedia<br>- 包含长答案（段落）和短答案（实体/日期等）<br>- 训练集 307k，开发/测试集各 8k | 评估开放域问答系统对真实用户查询的响应能力 | “谁写了《傲慢与偏见》？”（答案：“简·奥斯汀”） | | |
| | **MS MARCO**【12†L4-L10】 | - 基于 Bing 真实搜索查询的检索/问答基准<br>- 包含段落排序、文档排序、问答等任务<br>- 数据规模大（约 500k 查询，880 万段落） | 训练与评估检索模型在真实搜索场景下的表现 | “如何冲泡咖啡？”（系统需检索相关段落回答） | | |
| | **BEIR**【13†L4-L10】 | - 异构零样本检索基准，包含 18 个数据集、9 种任务（事实核查、QA、对话检索等）<br>- 评估模型在未见任务上的泛化能力<br>- 已成为检索模型的标准评估套件 | 评估检索模型在零样本设置下的跨任务泛化能力 | 给定查询“全球变暖的主要原因”，在文档集合中检索相关文档 | | |
| | **MIRACL**【14†L4-L10】 | - 多语言检索基准，覆盖 18 种语言（包括中文、阿拉伯语等）<br>- 专注于 ad‑hoc 检索，所有查询与文档均经过人工标注<br>- 提供多语言检索挑战（WSDM 2023 Cup） | 评估多语言检索模型在不同语言下的检索效果 | 中文查询：“全球变暖的原因”，检索相关中文文档 | | |
| --- | |
| ## 🧩 4. 组合搜索/推理类基准 | |
| 这类基准专门测试智能体在组合搜索问题上的逻辑推理与规划能力。 | |
| | 基准 | 核心特点 | 使用场景 | 例题 | | |
| |------|----------|----------|------| | |
| | **SearchBench**【30†L22-L36】 | - 11 种独特的搜索问题类型（路径查找、谜题、子集和、排序、欠定系统等）<br>- 自动生成任意数量实例，并评估解决方案的可行性、正确性、最优性<br>- GPT‑4 在纯文本模式下仅解决 1.4%，通过 A* 代码生成提升至 11.7% | 评估 LLM 在组合搜索问题上的推理与规划能力 | “在一个 5x5 迷宫中，从起点 (0,0) 到终点 (4,4) 的最短路径是什么？（障碍物位置已知）”【30†L56-L64】 | | |
| --- | |
| ## 🛒 5. 特定领域/仿真环境基准 | |
| 这类基准针对特定领域（如电商、家庭环境）设计，测试智能体在仿真环境中的交互能力。 | |
| | 基准 | 核心特点 | 使用场景 | 例题 | | |
| |------|----------|----------|------| | |
| | **WebShop**【29†L15-L30】 | - 模拟电商网站环境，包含 118 万真实商品、12,087 条众包指令<br>- 智能体需根据文本指令搜索、筛选、购买商品<br>- 奖励自动计算，最佳模型成功率 29%（人类 59%） | 评估语言智能体在电商任务中的导航、搜索与决策能力 | “找到一款价格低于 $50 的无线蓝牙耳机，评分至少 4 星，并加入购物车”【29†L20-L25】 | | |
| | **ALFWorld**【18†L4-L10】 | - 将文本环境（TextWorld）与具身环境（ALFRED）对齐的基准<br>- 智能体先在文本中学习抽象策略，然后在视觉环境中执行<br>- 包含 134 个家庭任务（如“在厨房找到杯子并拿到客厅”） | 评估智能体在文本与具身环境之间的迁移学习与规划能力 | “在厨房找到‘杯子’，把它拿到客厅放在桌子上” | | |
| --- | |
| ## 📊 总结 | |
| 以上基准覆盖了搜索智能体的不同能力维度：从**网页浏览、工具使用、问答检索**，到**组合搜索推理**以及**特定领域仿真**。选择合适的基准取决于您要评估的具体能力（如多模态交互、工具调用、多跳推理等）。这些基准大多提供公开的数据集与评估代码，可直接用于实验。 | |
| > 注：本文基于 2025 年 11 月前的公开资料整理，部分基准可能已有更新版本，建议访问对应官网获取最新信息。<｜end▁of▁sentence｜> |