The paper introduces a new corpus of 1,101 human simultaneous interpreting segments, each scored on meaning transfer, delivery quality, and perceived latency. It demonstrates that standard large‑language‑model prompting and scalar supervision fail to align with these rubric dimensions, producing near‑zero correlation with human ratings. By adding dual regression heads to a LoRA‑adapted COMET‑KIWI encoder, the authors achieve modest Pearson correlations (0.388 for meaning transfer and 0.301 for delivery quality) on a held‑out test set, improving over the frozen baseline.
arXiv:2601. 18253v2 Announce Type: replace-cross Abstract: Accurate evaluation of user satisfaction is critical for iterative development of conversational AI.
By Peng Sun, Xiangyu Zhang, Duan Wu, Lu Tan, Jian Lin, He Yang, Qi Qian, Yikai Wang
EviSI is an evidence‑based evaluation agent for low‑latency simultaneous speech‑to‑speech translation. It combines Multidimensional Quality Metrics with interpreter‑developed criteria, using shared source evidence to assess four dimensions—Anchor, Event, Logic, and Fluency—while deduplicating verified errors before scoring. On English‑to‑Chinese and Chinese‑to‑English data, EviSI’s rankings correlate strongly with human judgments, outperforming BLEU and COMET, and its multilingual extension maintains these correlations across five language directions.
By Ben Yan, Zongyao Li, Xiaoyu Chen, Daimeng Wei, Weidong Liu, Huan Zhao, Chong Li, Yaode Wang, Yuzhe Shang
arXiv:2609.22778v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require exper...
By Yuhan Lu, Yi Yao, Hua Shen, Katie Aafjes-van Doorn, Zhaonan Wang
arXiv:2601.08654v3 Announce Type: replace
Abstract: Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the...
By Yihan Hong, Huaiyuan Yao, Bolin Shen, Wanpeng Xu, Hua Wei, Yushun Dong
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 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
Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and evaluation across tasks and language pairs on one footing. We consolidate the WMT 2020--2024 shared-task lineage with an extended English--Malayalam resource into \indicqe: $126{,}754$ instances over nine directional pairs, with up to four label types aligned on the same segment, a direct assessment, a human post-edit, word-level OK/BAD tags and an error explanation, and a test set stratified over four difficulty axes.
arXiv:2608. 09930v1 Announce Type: cross Abstract: Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive.
By Oluwanifemi Bamgbose, Simon Rosen, Jash Shah, Lindsay Devon Brin, Hoang H Nguyen, Anke Koelzer, Rachel Hansen, Tara Bogavelli, Fanny Riols
arXiv:2608. 10385v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as relevance assessors in information retrieval (IR) evaluation, raising questions about how assessor framing affects judgment reliability and downstream system comparison.
By Samaneh Mohtadi, Pietro Bernardelle, Joel Mackenzie, Gianluca Demartini
EviSI is a large language model evaluation agent designed for simultaneous speech-to-speech translation. It adapts Multidimensional Quality Metrics to assess semantic fidelity and oral expression, using shared source evidence and deterministic scoring. In English‑to‑Chinese, EviSI achieves a mean Kendall agreement of 0.707 with human system rankings, outperforming baseline metrics, and shows positive concordance with COMET across five translation directions.
IndicQE-APE is a consolidated benchmark that unifies quality estimation (QE) and automatic post‑editing (APE) data for nine Indic language pairs, comprising 126,754 instances with multiple aligned labels such as direct assessment, human post‑edit, word‑level OK/BAD tags, and error explanations. The dataset includes a stratified test set across four difficulty axes and supports training and evaluation of six prompted large language models, three COMET metrics, and three APE systems. Experiments reveal that segments with conflicting holistic and token‑level quality signals are consistently ranked lower, while annotator disagreement shows no effect when controlled for score distribution.
whyItMatters":"The benchmark provides a unified resource for training and evaluating QE and APE across Indic languages, enabling consistent comparison of models and metrics on a shared dataset."
By Diptesh Kanojia, Archchana Sindhujan, Sourabh Deoghare, Daria Sokova, Shenbin Qian, Girish Koushik, Tharindu Ranasinghe, Constantin Or\u{a}san, Chrysoula Zerva, Ricardo Rei, Fr\'ed\'eric Blain, Andr\'e F. T. Martins, Marco Turchi, Matteo Negri, Anoop Kunchukuttan, Mitesh M. Khapra, Pushpak Bhattacharyya