Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs
To address this issue, we propose Omni-Woodpecker (OWP), a training-free framework that shifts hallucination mitigation from output-level manipulation to conflict-conditioned representation rebalancing.
ProofPaper ↗
Key points
- Recent Omni LLMs (OLLMs) have demonstrated remarkable capabilities in joint audio-visual reasoning, yet cross-modal hallucinations remain a major challenge.
- Existing mitigation methods mainly operate at the output level, overlooking the entanglement of grounding evidence and learned priors within fused representations.
- Specifically, OWP first identifies cross-modal conflicts from complementary modality-specific views.
- Finally, OWP adaptively rebalances fused representations by suppressing misleading priors and restoring ignored evidence before decoding.
Sources (1)
- [1]Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:01 PM
To address this issue, we propose Omni-Woodpecker (OWP), a training-free framework that shifts hallucination mitigation from output-level manipulation to conflict-conditioned representation rebalancing.
Recent Omni LLMs (OLLMs) have demonstrated remarkable capabilities in joint audio-visual reasoning, yet cross-modal hallucinations remain a major challenge.
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