arXiv cs.AI· Yi Zhang, Yi Li, Hongwei Du·· 1 天前AI 评分29
TRACE：对大型音频语言模型进行源级反事实审计
Right Answers, Inconsistent Evidence: Source-Level Counterfactual Auditing of Large Audio-Language Models
AI 导读
研究提出 TRACE，通过逐一删除被查询声源及其竞争声源，检验所有仍含该声音的视图得分是否高于删除后的视图，以此审计大型音频语言模型（LALM）是否真正区分声音的存在与缺失。
正文 · 原文
Abstract:A large audio-language model (LALM) can answer "is a cat present?" about a mixture correctly, and its confidence can drop when the cat is removed, yet this does not show that it can tell the cat's presence from its absence. We introduce TRACE. It deletes the queried source and each rival in turn and asks whether every view still containing that sound outscores the view with it removed. Across 8 LALMs and 3,456 controlled mixtures, 6.3% of correct answers fail this ordering: the target-absent view scores at or above some target-present view, a conflict no shared threshold repairs. A target-only score check misses 42.5% of them. The share rises from 4.9% to 8.4% as target SNR falls from +5 to -5 dB, even on units correct at every level, and survives loudness and re-pairing controls. Accuracy certifies the answer; TRACE audits whether it orders presence above absence. Code and data: this https URL.
| Comments: | 5 pages, 3 figures, 1 table. Submitted to ICASSP 2027 |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15463 [cs.SD] |
| (or arXiv:2611.15463v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15463 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yi Zhang [view email]
[v1]
Wed, 11 Nov 2026 13:32:23 UTC (185 KB)
来源：arXiv cs.AI · arxiv.org