arXiv:2609.05806v1 Announce Type: new
Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
By Amir Ben Khalifa, Fanny Bezancon, Amine Trabelsi, Bessam Abdulrazak
arXiv:2403.17612v3 Announce Type: replace
Abstract: Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus...
By Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa, Roman Klinger
arXiv:2606. 28772v1 Announce Type: cross Abstract: Hate speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training.
By Joshua Muhumuza, Joab Ezra Agaba, Mercy Amiyo
arXiv:2608.28932v1 Announce Type: new
Abstract: Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-o...
By Models Luc Debaupte, Tyler Baumgartner, Brandon Tai, Candice Fan, Bill Wang, Yi Zhong
Large language models (LLMs) are increasingly used to assess social bias in text, but the passages they evaluate often contain surface noise such as typos and broken punctuation. This study applied five realistic noise conditions at varying intensities to 3,822 stereotype‑related responses and compared bias judgments on noisy versus original text. The findings show that noise disproportionately turns neutral judgments into biased ones—up to 120 times more likely—while rarely converting biased judgments into neutral ones, and that the most fragile LLM judge exhibits the greatest distortion at mild noise levels. As LLMs become more robust, the bias distortion tends toward parity rather than reversal, meaning bias measured on noisy text is systematically overestimated, especially in fairness‑critical categories.
By DongHyun Ryu, Jaehyeok Lee, YeongJun Hwang, JinYeong Bak
Large language models used as judges for social bias are affected by noisy text, such as typos and broken punctuation. In experiments with 3,822 stereotype-related responses, noise more often turns neutral judgments into biased ones than the reverse, with up to a 120‑fold difference. The effect is strongest at mild realistic noise levels and leads to systematic overestimation of bias, especially in fairness‑critical categories.