On the Multi-turn Instruction Following for Conversational Web Agents
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
Web agents powered by Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within complex web-based environments, fulfilling a wide range of web navigation tasks. Despite these advancements, the potential for LLM-powered agents to effectively engage with sequential user instructions in real-world scenarios has not been fully explored. In this work, we introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment, supported by a specially developed dataset named Multi-Turn Mind2Web (MT-Mind2Web). To tackle the limited context length of LLMs and the context-dependency issue of the conversational tasks, we further propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques. Extensive experiments are conducted to benchmark the MT-Mind2Web dataset, and validate the effectiveness of the proposed method.
Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 34 of its 42 references that are in the map. First posted on arXiv 23 Feb 2024.
Builds on
34 of 42 references.
Closest ancestors · 5
Same direction, newest first
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- 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents4 Jul
Other references · 29
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- 2022PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Financecited by 45
- 2022HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Datacited by 28
- 2020FLIN: A Flexible Natural Language Interface for Web Navigationcited by 18
- 2023Context Aware Query Rewriting for Text Rankers using LLMcited by 14
- 2023"What's important here?": Opportunities and Challenges of Using LLMs in Retrieving Information from Web Interfacescited by 6
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Citing papers per year: 2024: 8; 2025: 22; 2026: 12.
42 papers.
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- 2026Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory29 May 2026
- 2026ReContraster: Making Your Posters Stand Out with Regional Contrast12 Apr 2026
- 2025Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards21 Oct 2025
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