Clinical audio models are often trained by aligning recordings with participant metadata or text derived from it. This metadata is rarely a clean disease signal, since a single profile also contains symptoms, age, sex, missing responses, and recruitment information. We examine what alignment adds to frozen audio features by varying only the audio-meta data pairing. We keep the audio backbones fixed and train only a projector under correct pairing and three shuffled conditions that progressively preserve the disease label and recorded sex. We then evaluate each projector on profile retrieval, attribute probing, and disease classification on covariate-matched test sets. On UKCOVID, correct pairing clearly improves retrieval and makes sex easier to decode, confirming that the pairing manipulation works. Most of the sex-probe gap disappears when the control also preserves recorded sex, while a small retrieval gap remains. Matched disease AUROC differences are small, and every interval includes zero, so modest benefits or harms are not ruled out. The same pattern appears when we rerun the frozen pipeline on CODA TB, Cambridge COVID-19 Sounds, and Coswara, where retrieval and sex-probe contrasts are consistently positive but disease contrasts are not. We find no consistent evidence that better profile correspondence translates into better disease prediction and suggest reporting these outcomes separately.
Jiawei Cao, Jie Ouyang University of Science and Technology of China, Mingyue Cheng University of Science and Technology of China, Zhaomeng Zhou, Chunli Liu Hefei University of Technology, Yupeng Li University of Science and Technology of China, Zirui Liu, Shijin Wang State Key Laboratory of Cognitive Intelligence
Retrieval-Augmented Generation (RAG) is a mainstream approach to mitigating hallucinations in Large Language Models (LLMs), yet in dynamic real-world scenarios, such as weather forecasting or evolving news events, existing retrievers suffer from both temporal-semantic misalignment and outdated-document interference. To address this, we propose Relevance Recency Retrieval (Re^3), a novel framework that mitigates temporal hallucinations via two core components: a Time-Aware Dual Relevance Encoder that embeds heterogeneous temporal signals into the semantic space to ensure retrieval fidelity, and a Conflict-Aware Recency Filter that performs listwise arbitration to identify and suppress obsolete factual versions. To rigorously evaluate this setting, we introduce Re^2 Bench, a large-scale benchmark comprising over 1.3 million instances designed to assess system robustness in realistic environments where temporal constraints and conflicting factual versions coexist. Experiments on three public benchmarks and Re^2 Bench demonstrate that Re^3 consistently outperforms the strongest baselines by an average of 9.7% in generation accuracy, with gains of up to 25.2% on challenging dynamic tasks, while demonstrating robustness across diverse RAG settings.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Chongyuan Dai, Yaling Shen Monash University, Zihan Gao, Jia Li Hefei University of Technology, Yishun Jiang University of Science and Technology of China, Yaxiong Wang Hefei University of Technology, Liu Liu Hefei University of Technology, Zongyuan Ge 等 9 位作者
Culture serves as a fundamental determinant of human affective processing and profoundly shapes how individuals perceive and interpret emotional stimuli. Despite this intrinsic link extant evaluations regarding cultural alignment within Large Language Models primarily prioritize declarative knowledge such as geographical facts or established societal customs. These benchmarks remain insufficient to capture the subjective interpretative variance inherent to diverse sociocultural lenses. To address this limitation, we introduce CEDAR, a multimodal benchmark constructed entirely from scenarios capturing Culturally Elicited Distinct Affective Responses. To construct CEDAR, we implement a novel pipeline that leverages LLM-generated provisional labels to isolate instances yielding cross-cultural emotional distinctions, and subsequently derives reliable ground-truth annotations through rigorous human evaluation. The resulting benchmark comprises 10,962 instances across seven languages and 14 fine-grained emotion categories, with each language including 400 multimodal and 1,166 text-only samples. Comprehensive evaluations of 17 representative multilingual models reveal a dissociation between language consistency and cultural alignment, demonstrating that culturally grounded affective understanding remains a significant challenge for current models.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表