Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs explores A novel framework for aligning Large Vision-Language Models by enabling self-correction to mitigate hallucinations, requiring significantly fewer samples.. Commercial viability score: 7/10 in LVLM Alignment.
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Canonical route: /paper/aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlms
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Canonical ID aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlms | Route /paper/aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlms
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlmsMCP example
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}Paper proof page receipt window
/buildability/aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlms
Subject: Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs
Verdict
Watch
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Dimensions overall score 7.0
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Receipt path
/buildability/aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlms
Paper ref
aligning-with-your-own-voice-self-corrected-preference-learning-for-hallucination-mitigation-in-lvlms
arXiv id
2604.24395
Generated at
2026-04-28T15:17:54.209Z
Evidence freshness
fresh
Last verification
2026-04-28T15:17:54.209Z
Sources
3
References
0
Coverage
50%
Lineage hash
2c961532984784febe825742da9cfc276213dd0b8fd276c88ddcd4663068e26e
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unsigned_external
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references
This equation captures one of the core mathematical components of the system. ction that directly optimizes the policy using a preference dataset D = {x, yw, yl}
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This equation captures one of the core mathematical components of the system. LDPO(πθ; πref) = −E(x,yw,yl)∼D [log σ (r(x, yw) −r(x, yl))] log σ β log πθ(yw|x)
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This equation captures one of the core mathematical components of the system. r(x, y) = β log πθ(y|x) πref(y|x) + β log Z(x). Here, x denotes the multimodal
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