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https://arxiv.org/html/2411.14432v1#S3간단하게 두개의 MLLM 사용 reasoning, summarizationTo fully leverage the reasoning capabilities of MLLMs, we propose Insight-V, a novel system comprising two MLLMs dedicated to reasoning and summarization, respectively. reasoning model - detailed reasoning process 생성summary model - reasoning을 supplementray info 보조적인 정보로 사용해 정답에 대한 relevance utilility 를 평가  3.2 Constru..

https://arxiv.org/html/2412.09078v1#S4 Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM ReasoningForest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning Zhenni Bi    Kai Han    Chuanjian Liu    Yehui Tang    Yunhe Wang Abstract Large Language Models (LLMs) have shown remarkable abilities across various language tasks,arxiv.orgbenchmark : GSM 8k , MATH 3.1 FoT frame..

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has emerged as indicate represent denote  identify despite, nevertheless, However, Although advances, development, advancemente.g) despite these advances,  have demonstrated shown propose present compelling comparable superior  significant remarkable impressive ~ing, thereby, thuse.g) Mastering multi-step visual reasoning requires the integration of multimodal information, along with rigorous ad..

https://aclanthology.org/2024.tacl-1.78/ When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsRyo Kamoi, Yusen Zhang, Nan Zhang, Jiawei Han, Rui Zhang. Transactions of the Association for Computational Linguistics, Volume 12. 2024.aclanthology.orgWhen Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs https://arxiv..

Efficient self-improvement in multimodal large language models: A model-level judge-free approach. Strengthening multimodal large language model with bootstrapped preference optimization. CLIP-DPO: Vision-language models as a source of preference for fixing hallucinations in lvlms. Enhancing large vision language models with self-training on image comprehension. RLAIF-V: Aligning mllms through o..

https://arxiv.org/html/2412.17451v1#bib.bib33

https://arxiv.org/pdf/2412.03704v2

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https://arxiv.org/html/2501.07542v1