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Survey details LLM agent evaluation for multi-turn conversations

By PulseAugur Editorial · [1 sources] ·

A new survey published on arXiv details evaluation methods for large language model (LLM)-based agents in multi-turn conversations. The paper, structured using a PRISMA-inspired framework, systematically reviewed nearly 250 scholarly sources to establish the state of the art. It introduces two taxonomies: one defining what aspects to evaluate, such as task completion and user experience, and another outlining how to evaluate, including annotation-based methods, automated metrics, and hybrid strategies. AI

IMPACT Provides a structured framework for assessing LLM conversational agents, potentially guiding future research and development in agent evaluation.

RANK_REASON The item is a survey paper published on arXiv detailing evaluation methods for LLM-based agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details LLM agent evaluation for multi-turn conversations

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The item is a survey paper published on arXiv detailing evaluation methods for LLM-based agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shengyue Guan, Jindong Wang, Jiang Bian, Bin Zhu, Jian-guang Lou, Haoyi Xiong ·

    Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey

    arXiv:2503.22458v3 Announce Type: replace-cross Abstract: This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings. Using a PRISMA-inspired framework, we systematically reviewed nearly 250 scholarly sources, capturi…

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