The paper introduces UPHELD, a large benchmark of human-to-human dialogues written by professional script writers, featuring realistic turn densities and over 36,000 per-turn human annotations. It evaluates existing automatic metrics and LLM-as-a-judge methods, finding them unreliable against expert human judgment. Using UPHELD, the authors develop a Mixture-of-Judges framework that improves correlation with human assessments by about 30%.
By Ilija Subasic, Andrew Rabinovich, Zhao Chen
The paper introduces Evaluation-as-Search (EaS), a feedback‑driven method that adaptively probes LLM‑powered meeting assistants by focusing on natural questions likely to reveal grounding failures. Using EaS, the authors build MeetingProbe, a benchmark of over 3,000 annotated question‑answer pairs from 20 transcripts across three meeting genres and three assistants. Ablation studies show that adaptive search uncovers 2.5× more failures than random probing, revealing a capability gradient and eight recurring failure categories dominated by discourse‑pragmatic challenges.
By Sami Khairy, Yasaman Hosseinkashi, Vishak Gopal, Ross Cutler
arXiv:2606. 08445v1 Announce Type: cross Abstract: Meeting documents are challenging to summarize due to their length and complex conversational structure.
By Sangwon Ryu, Heejin Do, Jun Seo, Daehui Kim, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok
arXiv:2609.26780v1 Announce Type: cross
Abstract: Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distingu...
By Haobo Zheng, Tan Tang, Yan Chen, Weijie Wang, Yingcai Wu
arXiv:2606. 27226v1 Announce Type: new Abstract: Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug.
By Sangwoo Cho, Kushal Chawla, Pengshan Cai, Zefang Liu, Chenyang Zhu, Shi-Xiong Zhang, Sambit Sahu
arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.
By Niranjan Kumar M, Balaji Nagarajan, Karthik Nair, Faysal Satter, Nithin Surendran
The paper introduces LongJudgeBench, a benchmark designed to evaluate large language models (LLMs) acting as judges for long-form text generation. It highlights that long-form evaluation requires complex, document-level assessments beyond simple length, such as organization, coverage, depth, consistency, and scenario-specific quality. Experiments show a significant reliability gap among current LLM judges, indicating instability across scenarios and limited effectiveness of rubrics or references.
By Junjie Chen, Yuxi Dong, Haitao Li, Weihang Su, Yujia Zhou, Min Zhang, Yiqun Liu, Qingyao Ai
arXiv:2608.29834v1 Announce Type: new
Abstract: Collaborative conversations frequently contain references whose targets are indirect rather than named: resolving "this looks like the fix discussed ye...
By Karen Fuchs, Uri Katz, Yoav Goldberg
arXiv:2606. 04442v1 Announce Type: cross Abstract: AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents.
By Qiyang Xie, Jialun Wu, Xinjie He, Su Liu, Shuai Xiao, Zhiyuan Lin, Weikai Zhou
arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
By Noam Koren, Roy Bar-Haim, Abigail Goldsteen
AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously.
arXiv:2507.01594v2 Announce Type: replace
Abstract: Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD sy...
By Shutong Feng, Hsien-chin Lin, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, Milica Ga\v{s}i\'c