Fine-tuning LLMs on the SubPOP dataset of 3,362 questions and 70K pairs reduces the gap between LLM predictions and human survey responses by up to 46% and generalizes to unseen surveys and subpopulations.
arXiv preprint arXiv:2406.05882
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Mixed Preference Optimization with the MMPR dataset boosts multimodal CoT reasoning, lifting InternVL2-8B to 67.0 accuracy on MathVista (+8.7 points) and matching the 76B model.
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.
citing papers explorer
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Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
Fine-tuning LLMs on the SubPOP dataset of 3,362 questions and 70K pairs reduces the gap between LLM predictions and human survey responses by up to 46% and generalizes to unseen surveys and subpopulations.
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Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization
Mixed Preference Optimization with the MMPR dataset boosts multimodal CoT reasoning, lifting InternVL2-8B to 67.0 accuracy on MathVista (+8.7 points) and matching the 76B model.
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OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.