AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives (2026.findings-acl)
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Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang
| Challenge: | Large Audio-Language Models suffer from hallucinations, e.g., generating text not grounded in the audio input. |
| Approach: | They propose a framework to address hallucination problems in large audio-language models . they use a preference dataset to test the model's accuracy . |
| Outcome: | The proposed model outperforms the latest SOTA methods in terms of performance and generalization. |
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