Papers with MultiChallenge
MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs (2025.findings-acl)
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Kaustubh Deshpande, Ved Sirdeshmukh, Johannes Baptist Mols, Lifeng Jin, Ed-Yeremai Hernandez-Cardona, Dean Lee, Jeremy Kritz, Willow E. Primack, Summer Yue, Chen Xing
| Challenge: | Existing evaluation frameworks for large language models have limited coverage for multi-turn conversations . multi-turned conversations require accurate instruction following, context allocation, and in-context reasoning at the same time. |
| Approach: | They propose a benchmark to evaluate large language models' ability to conduct multi-turn conversations with humans. |
| Outcome: | The proposed benchmarks achieve near perfect scores on existing benchmarks but only a 41.4% accuracy on the frontier models. |
Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)
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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He
| Challenge: | End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. |
| Approach: | They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns. |
| Outcome: | The proposed model fails on the highest-performing model with 54.65% pass rate. |