Improve Vision Language Model Chain-of-thought Reasoning

• URL: http://arxiv.org/abs/2410.16198v1
• Date: Mon, 21 Oct 2024 17:00:06 GMT
• Title: Improve Vision Language Model Chain-of-thought Reasoning
• Authors: Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, Yiming Yang,
• Abstract summary: Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. We show that training VLM on short answers does not generalize well to reasoning tasks that require more detailed responses.
• Score: 86.83335752119741
• License:
• Abstract: Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT reasoning data, relying on datasets dominated by short annotations with minimal rationales. In this work, we show that training VLM on short answers does not generalize well to reasoning tasks that require more detailed responses. To address this, we propose a two-fold approach. First, we distill rationales from GPT-4o model to enrich the training data and fine-tune VLMs, boosting their CoT performance. Second, we apply reinforcement learning to further calibrate reasoning quality. Specifically, we construct positive (correct) and negative (incorrect) pairs of model-generated reasoning chains, by comparing their predictions with annotated short answers. Using this pairwise data, we apply the Direct Preference Optimization algorithm to refine the model's reasoning abilities. Our experiments demonstrate significant improvements in CoT reasoning on benchmark datasets and better generalization to direct answer prediction as well. This work emphasizes the importance of incorporating detailed rationales in training and leveraging reinforcement learning to strengthen the reasoning capabilities of VLMs.

Related papers

• What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? [83.83230167222852]
  We find that a model's generalization behavior can be effectively characterized by a training metric we call pre-memorization train accuracy. By connecting a model's learning behavior to its generalization, pre-memorization train accuracy can guide targeted improvements to training strategies.
  arXiv  Detail & Related papers  (2024-11-12T09:52:40Z)
• RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner [2.779063752888881]
  Self-taught reasoner (STaR) framework uses reinforcement learning to automatically generate reasoning steps. STaR and its variants have demonstrated empirical success, but a theoretical foundation explaining these improvements is lacking. This work provides a theoretical framework for understanding the effectiveness of reinforcement learning on CoT reasoning and STaR.
  arXiv  Detail & Related papers  (2024-10-31T13:17:53Z)
• Vision-Language Models Can Self-Improve Reasoning via Reflection [20.196406628954303]
  Chain-of-thought (CoT) has proven to improve the reasoning capability of large language models (LLMs) We propose a self-training framework, R3V, which iteratively enhances the model's Vision-language Reasoning by Reflecting on CoT Rationales. Our approach supports self-reflection on generated solutions, further boosting performance through test-time computation.
  arXiv  Detail & Related papers  (2024-10-30T14:45:00Z)
• Improving Language Model Reasoning with Self-motivated Learning [60.779625789039486]
  textitSelf-motivated Learning framework motivates the model itself to automatically generate rationales on existing datasets. We train a reward model with the rank to evaluate the quality of rationales, and improve the performance of reasoning through reinforcement learning.
  arXiv  Detail & Related papers  (2024-04-10T14:05:44Z)
• Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models [61.28463542324576]
  Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can generate human-like outputs. We evaluate existing state-of-the-art VLMs and find that even the best-performing model is unable to demonstrate strong visual reasoning capabilities and consistency. We propose a two-stage training framework aimed at improving both the reasoning performance and consistency of VLMs.
  arXiv  Detail & Related papers  (2023-09-08T17:49:44Z)
• Entailment as Robust Self-Learner [14.86757876218415]
  We design a prompting strategy that formulates a number of different NLU tasks as contextual entailment. We propose the Simple Pseudo-Label Editing (SimPLE) algorithm for better pseudo-labeling quality in self-training.
  arXiv  Detail & Related papers  (2023-05-26T18:41:23Z)
• SCOTT: Self-Consistent Chain-of-Thought Distillation [68.40232422158569]
  Large language models (LMs) generate free-text rationales for their predictions via chain-of-thought prompting. We propose a faithful knowledge distillation method to learn a small, self-consistent CoT model from a teacher model that is orders of magnitude larger. To ensure faithful distillation, we use the teacher-generated rationales to learn a student LM with a counterfactual reasoning objective.
  arXiv  Detail & Related papers  (2023-05-03T03:47:00Z)
• Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach [6.244905619201076]
  In this work, model explanations are fed back to the feed-forward training to help the model generalize better. The framework incorporates the custom weighted loss with Elastic Weight Consolidation (EWC) to maintain performance in sequential testing sets. The proposed custom training procedure results in a consistent enhancement of accuracy ranging from 0.5% to 1.5% throughout all phases of the incremental learning setup.
  arXiv  Detail & Related papers  (2022-11-02T18:16:17Z)
• VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives [84.48039784446166]
  We show that model FI supervision can meaningfully improve VQA model accuracy as well as performance on several Right-for-the-Right-Reason metrics. Our best performing method, Visual Feature Importance Supervision (VisFIS), outperforms strong baselines on benchmark VQA datasets. Predictions are more accurate when explanations are plausible and faithful, and not when they are plausible but not faithful.
  arXiv  Detail & Related papers  (2022-06-22T17:02:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.