The paper presents an interpretable, fair, and accurately benchmarked automated system for assessing second‑language English speaking. Using a hybrid of feature‑based speech‑timing metrics and a large language model (LLM) fluency judgment, the system achieves a Spearman correlation of 0.818 with the ICNALE Global Rating Archive, outperforming 81 % of trained human raters. A controlled study shows that encoding pauses into the LLM prompt does not meaningfully affect fluency scores, indicating that the system’s fluency signal derives from measurable speech‑timing features.
By Eichi Uehara
The study examined how four large language models (GPT‑5.5, Gemini 3.5 Flash, Claude Opus 4.8, and Fable 5) scored 18 simulated Japanese‑language AI‑to‑AI counseling sessions compared to ratings from 15 human counseling experts. Each model evaluated every transcript three times on four motivational‑interviewing‑informed dimensions and overall quality, consistently giving higher scores for softening sustain talk and overall quality than the expert panel, though the magnitude varied by model. Run‑to‑run reliability (intraclass correlation coefficients ranging from .33 to .96) did not predict closer alignment with expert judgments, and the models’ ability to discriminate counselor conditions was distinct from both reliability and alignment.
By Keita Kiuchi, Yoshikazu Fujimoto, Hideyuki Got\=o, Tomonori Hosokawa, Makoto Nishimura, Y\=osuke Sat\=o, Izumi Sezai, Tomohiro Inoue
arXiv:2609.13150v1 Announce Type: cross
Abstract: Reference-free quality predictors such as UTMOS, DNSMOS and SCOREQ are the de facto automatic evaluators for text-to-speech (TTS) and are increasingl...
By Antonis Asonitis, Juan Pablo Zuluaga Gomez, Francesco Verdini, Aref Farhadipour, Marzieh Razavi, Pierre-Edouard Honnet, Vijeta Avijeet
arXiv:2609.13773v1 Announce Type: new
Abstract: LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may...
By Ruichen Zheng, Yihe Wang, Fabrice Y Harel-Canada, Sara Khosravi, Zeynep Senahan Yildiz, Amit Sahai, Nanyun Peng
The paper evaluates nine on‑device named‑entity recognition models ranging from classical taggers to large language models, measuring not only accuracy but also latency and output validity. Using a silver‑gold benchmark derived from an LLM judge panel and a human‑validated corpus, the study shows that encoder‑based models achieve comparable accuracy to a 4 B instruct LLM while being much smaller, faster, and producing no malformed output. Confidence calibration of GLiNER is analyzed, revealing over‑confidence but improved reliability after temperature scaling and thresholding.
By Vinay Kumar Chaganti
arXiv:2606. 23915v1 Announce Type: cross Abstract: Practice often treats automatic metrics for attribution in LLM retrieval-augmented generation as interchangeable.
By Tianyu Ding, Aditya Nannapaneni, Juan Pablo De la Cruz Weinstein