Lin Zhang, You Zhang, Nicholas Evans, Xin Wang, David Harwath, Abhinav Dhall, Bowen Shi, Chengzhe Sun 等 10 位作者
Recent advances in generative audio models facilitate increasingly flexible and selective editing of linguistic content and paralinguistic, extralinguistic, and other attributes. This paradigm, named partially edited audio, differs from full audio generation as only portions of the real recording are edited. This technique can serve both beneficial and harmful applications. We therefore present a concise survey from both generation and defense perspectives, focusing on audio, as a contribution to the ICASSP 2027 special session entitled "Partially Edited Media: Perspectives from Synthesis and Defense." We first define partially edited audio and argue that what counts as ``fake'' or harmful is an application-dependent judgment, not a fixed property of the edit. We then survey audio editing by editing target, approaches, and evaluation, and then defense methods by detection, localization, diarization, and interpretability, summarizing representative benchmarks and toolkits. Finally, we discuss future directions and open problems.
Ruifeng Yuan, Wanxing Chang, Weiwei Cao Alibaba Group, Bowen Shi Shanghai Jiao Tong University and Alibaba DAMO Academy, Alibaba Group, Zhongyu Wei Fudan University, Ling Zhang Alibaba Group, Jianpeng Zhang
The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the presence of fine-grained, disease-oriented attributes. Conventional evaluation metrics offer only coarse measures of lexical overlap or entity matching and fail to reflect the granular diagnostic accuracy required for clinical use. To address this gap, we propose CT-FineBench, a benchmark built from CT-RATE and Merlin to evaluate the fine-grained factual consistency of CT reports, constructed from CT-RATE and Merlin. Our benchmark is constructed through a meticulous, Question-Answering (QA) based process: first, we identify and structure key, finding-specific clinical attributes (e.g., location, size, margin). Second, we systematically transform these attributes into a QA dataset, where questions probe for specific clinical details grounded in gold-standard reports. The evaluation protocol for CT-FineBench involves using this QA dataset to query a machine-generated report and scoring the correctness of the answers. This allows for a comprehensive, interpretable, and clinically-relevant assessment, moving beyond superficial lexical overlap to pinpoint specific clinical errors. Experiments show that CT-FineBench correlates better with expert clinical assessment and is substantially more sensitive to fine-grained factual errors than prior metrics.
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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
Huachen Qi, Ruiyu Zhuo, Bowen Shi Xiamen University, Xiang Chang University of Wales, Aberystwyth, Fei Chao Xiamen University, Changjing Shang Aberystwyth University, Qiang Shen Aberystwyth University
Large Language Models continue to scale in size and capability, driving substantial computational and memory demands.Mixture-of-Experts (MoE) architectures alleviate this cost by activating only a sparse subset of experts per token, enabling efficient scaling without proportional increases in inference compute.However, quantization in MoE models remains challenging due to heterogeneous sensitivity across experts and their internal linear layers.Existing mixed-precision frameworks such as Mixed-precision Quantization for MoE (MxMoE) require full quantization-loss evaluation for expert–layer–and-bit configurations, incurring prohibitive profiling cost.To address this, we propose FRI-MxMoE, a profiling-free mixed-precision quantization framework built on Fuzzy Rule Interpolation, designed as a drop-in replacement for the loss estimation component in MxMoE. By constructing a fuzzy rule base in the intra-expert layer feature space (bit-width, activation variance, parameter scale), our method predicts quantization error from only sparse samples, eliminating the need for dense profiling.Extensive experiments demonstrate that FRI-MxMoE accelerates the profiling phase by up to 15.7\times (on DeepSeek-V2) while achieving comparable or slightly superior zero-shot accuracy (e.g., +1.04\% on DeepSeekV2-Lite) compared to the baseline.This enables continuous sensitivity modeling, preserves accuracy under mixed-precision allocation, and reduces offline computation by orders of magnitude.
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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