MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs
Open accessAbstract
Kaustubh Deshpande, Ved Sirdeshmukh, Johannes Baptist Mols, Lifeng Jin, Ed-Yeremai Hernandez-Cardona, Dean Lee, Jeremy Kritz, Willow E. Primack, Summer Yue, Chen Xing. Findings of the Association for Computational Linguistics: ACL 2025. 2025.
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Existing benchmarks lack systematic evaluation of large language models across diverse memory dimensions, particularly in multi-session settings.
“Despite recent advances in understanding and leveraging long-range conversational memory, existing benchmarks still lack systematic evaluation of large language models(LLMs) across diverse memory dimensions, particularly in multi-session settings.”
From EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory (2026) · cites this paper as Deshpande et al., 2025
The Introduction says existing multi-turn benchmarks no longer discriminate effectively among frontier LLMs, supporting a general benchmark gap. The Abstract describes MultiChallenge’s four multi-turn challenges, but the cited passages do not establish a lack of systematic evaluation across diverse memory dimensions, particularly in multi-session settings. Searches for those terms found no supporting passage in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThis mixture follows interaction patterns observed in prior conversational benchmarks.
“This mixture follows interaction patterns observed in prior conversational benchmarks (Wu et al., 2024;Deshpande et al., 2025).”
From From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents (2026) · cites this paper as Deshpande et al., 2025
The Introduction (page not available) describes MultiChallenge as a benchmark of realistic multi-turn conversations and identifies four recurring challenges in human–LLM interactions. That supports the general reference to conversational interaction patterns, but the paper does not establish that the citing document’s unspecified mixture follows those patterns. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe mixture of memory-neutral and memory-grounded sessions follows interaction patterns observed in prior conversational benchmarks.
“This mixture follows interaction patterns observed in prior conversational benchmarks (Wu et al., 2024; Deshpande et al., 2025).”
From From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents (2026) · cites this paper as Deshpande et al., 2025
Section 3.1.2 describes conversations in which a model must use user details from earlier turns, supporting the memory-grounded part of the claim. The Introduction also describes other conversational challenges, but our copy does not identify any sessions as memory-neutral or say that a mixture of memory-neutral and memory-grounded sessions is an observed benchmark pattern. Page numbers were not available in the indexed passages. (LLM, conf=0.81)
Experimental — accuracy not yet measuredPerLTQA, Multichallenge, and MemoryAgentBench employ a multi-turn format closer to the basic pattern of interaction than long text input.
“Furthermore, PerLTQA (Du et al., 2024), Multichallenge (Deshpande et al., 2025), and MemoryAgentBench (Hu et al., 2025) employ a multi-turn format closer to the basic pattern of interaction than long text input.”
From EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory (2026) · cites this paper as Deshpande et al., 2025
Most existing evaluations assess memory either within a single session or through single-turn long-context inputs.
“First, most existing evaluations assess memory either within a single session (Deshpande et al., 2025; Hu et al., 2025) or through single-turn long-context inputs (Hsieh et al., 2024; Modarressi et al., 2025; Du et al., 2024).”
From EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory (2026) · cites this paper as Deshpande et al., 2025
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- Authors
- Deshpande, K., Sirdeshmukh, V., Mols, J. B., Jin, L., Hernandez-Cardona, E.-Y., Lee, D., Kritz, J., Primack, W. E., Yue, S., & Xing, C.
- Venue
- Findings of the Association for Computational Linguistics: ACL 2025
- Published
- 2025
- Pages
- 18632-18702
- OpenAlex
- W4412888095
- Cited by
- 13
- References
- 0
- Citations by year
- 2026: 12 · 2025: 1
- FWCI
- 12.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- multi-turn dialogue, large language models, conversational AI, dialogue evaluation
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