Papers by Xiaojuan Qi

2 papers
Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (2025.findings-emnlp)

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Challenge: Existing methods for hallucination mitigation are based on external dependency and require external annotations or auxiliary models for preference data collection.
Approach: a new method is proposed to help model-generated hallucinations without external dependencies.
Outcome: a new method that self-injects hallucinations into a generated response improves halluuutations mitigation.
TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization (2026.acl-long)

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Challenge: Current reward models for reinforcement learning (RL) rely on outcome rewards that propagate a single scalar value across all tokens based on final correctness.
Approach: They propose a framework that derives dense token-level supervision from LLMs . they use a multi-granularity calibration mechanism to modulate teacher influence .
Outcome: The proposed framework evaluates teacher reliability across problem-level expertise, trajectory-level discrimination, and token-level confidence.

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