Yan Fang The Hong Kong University of Science and Technology, Jun Chen The Hong Kong University of Science and Technology (Guangzhou), Yian Yao, Shuxin Zhong The Hong Kong University of Science and Technology, Min Sun China Mobile Communications Company, Kaishun Wu
Severe acoustic degradation is often caused by overlapping noise, disfluencies, and environmental distortions. This phenomenon results in the dissolution of linguistic structures and the generation of unreliable ASR outputs. Inspired by human speech comprehension, we propose Speech-MLM, a novel multimodal framework that reframes ASR as semantics-guided speech reconstruction. This perspective introduces three core challenges: (C1) collapse of linguistic structure under acoustic degradation, (C2) semantic ambiguity under noise, and (C3) misalignment across modalities. To address these issues, we propose Speech-MLM, a multimodal ASR framework that integrates speech, spectrogram-derived visual cues, and textual variants to enhance robustness. It consists of: (i) Cognitive Structure Extractor that recovers prosodic structure from visualized acoustic features, (ii) Semantic Weaver that learns semantic equivalence across varied textual forms, and (iii) Retrieval-Guided Fusion Learner that unifies modalities within a shared semantic space. Experiments on multiple real-world noisy datasets demonstrate that Speech-MLM achieves an average 38.85% reduction in WER, while also attaining 98.71% BERTScore and 96.7% USE, over advanced baselines, demonstrating substantial gains in semantic robustness and generalization across domains.
尚无 AI 解读，展示原文摘要
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
Yanbei Jiang, Amr Keleg Mohamed bin Zayed University of Artificial Intelligence, Ryandito Diandaru, Jey Han Lau The University of Melbourne, Lea Frermann University of Melbourne, Biaoyan Fang Oracle, Fajri Koto Mohamed bin Zayed University of Artificial Intelligence
While the real world is inherently stochastic, Large Language Models (LLMs) are predominantly evaluated on single-round inference against fixed ground truths. In this work, we shift the lens to distribution alignment: assessing whether LLMs, when prompted repeatedly, can generate outputs that adhere to a desired target distribution, e.g. reflecting real-world statistics or a uniform distribution. We formulate distribution alignment using the attributes of gender, race, and sentiment within occupational contexts. Our empirical analysis reveals that off-the-shelf LLMs and standard alignment techniques, including prompt engineering and Direct Preference Optimization, fail to reliably control output distributions. To bridge this gap, we propose a novel fine-tuning framework that couples Steering Token Calibration with Semantic Alignment. We introduce a hybrid objective function combining Kullback-Leibler divergence to anchor the probability mass of latent steering tokens and Kahneman-Tversky Optimization to bind these tokens to semantically consistent responses. Experiments across six diverse datasets demonstrate that our approach significantly outperforms baselines, achieving precise distributional control in attribute generation tasks.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表