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"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models
The evaluation of factual accuracy in large vision language models (LVLMs) has lagged behind their rapid development, making it challenging to fully reflect these models' knowledge capacity and reliability.
Performance Analysis of Traditional VQA Models Under Limited Computational Resources
In real-world applications where computational resources are limited, effectively integrating visual and textual information for Visual Question Answering (VQA) presents significant challenges.
Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation
Direct Preference Optimization (DPO) has been demonstrated to be highly effective in mitigating hallucinations in Large Vision Language Models (LVLMs) by aligning their outputs more closely with human preferences.
2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision
Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of Large Language Models (LLMs) with human preferences, owing to its simplicity and effectiveness.
SARA: Singular-Value Based Adaptive Low-Rank Adaption
However, the rank values need to be manually verified to match different downstream tasks, and they cannot accommodate the varying importance of different layers in the model.
