Papers by Peijie Qiu

2 papers
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives (2026.findings-acl)

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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.
DRA-GRPO: Your GRPO Needs to Know Diverse Reasoning Paths for Mathematical Reasoning (2026.findings-acl)

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Challenge: Existing methods for group-relative policy optimization rely on scalar correctness rewards that are often non-injective with respect to semantic content.
Approach: They propose a framework that calibrates the reward signal using the semantic density of sampled groups.
Outcome: The proposed framework outperforms strong baselines on five math benchmarks with 7,000 samples and 55 cost.

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