Papers with MTR-Bench
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation (2026.acl-long)
Copied to clipboard
Xiaoyuan Li, Keqin Bao, Yubo Ma, Moxin Li, Wenjie Wang, Rui Men, Yichang Zhang, Fuli Feng, Dayiheng Liu
| Challenge: | Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. |
| Approach: | They propose to use a multi-turn reasoning evaluation framework to cover multi-turn interactions with the environments of large language models. |
| Outcome: | The proposed framework covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments. |
MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)
Copied to clipboard
Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu
| Challenge: | Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics. |
| Approach: | They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval. |
| Outcome: | The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power. |