arXiv AI
Jun 10

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty

arXiv:2602. 12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field.

By Ziqian Zhang, Xingjian Hu, Yue Huang, Kai Zhang, Ruoxi Chen, Yixin Liu, Qingsong Wen, Kaidi Xu, Xiangliang Zhang, Neil Zhenqiang Gong, Lichao Sun
arXiv AI
Sep 21

Chinese Competitive Debating Dataset and Benchmark

The paper introduces a Chinese competitive debating dataset and benchmark, comprising 148 fully adjudicated matches with 2,698 stages and 20,542 exchange units, all annotated by 120 professional judges using a shared rubric. It offers manually verified transcripts, segmentation, and preserved scores, votes, and rationales, enabling evaluation of large language models on winner-tendency prediction, stage-score prediction, and best-debater prediction. Zero‑shot experiments show the best models achieve 66.2% winner‑prediction accuracy, a 0.250 Pearson correlation for stage scores, and 56.8% best‑debater accuracy.

By Zongrui Yang, Haoyuan Li, Zhongsheng Wang, Zhirui Zeng, Pengqian Han, Yi Zhou, Yuting Wang, Jiamou Liu
arXiv Computation and Language
Sep 16

EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpreting

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