PStar adaptively selects pseudocode-based reasoning strategies via a Difficulty Feature Vector to reduce hallucinations in vision-language models, reporting SOTA results on POPE and MMStar benchmarks.
Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation,
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Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models
PStar adaptively selects pseudocode-based reasoning strategies via a Difficulty Feature Vector to reduce hallucinations in vision-language models, reporting SOTA results on POPE and MMStar benchmarks.