6 papers
Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
Mingda Li, Rundong Lv, Xinyu Li +2
Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate. Existing state-of-the-art UQ approaches for…
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
Qiming Li, Zekai Ye, Xiaocheng Feng +9
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, l…
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems
Zekun Zhou, Xiaocheng Feng, Lei Huang +11
Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development…
FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging
Zijian Li, Xiaocheng Feng, Huixin Liu +3
With the development of large language models, fine-tuning has emerged as an effective method to enhance performance in specific scenarios by injecting domain-specific knowledge. I…
CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models
Qiming Li, Zekai Ye, Xiaocheng Feng +8
Although Large Vision-Language Models (LVLMs) have demonstrated powerful capabilities in interpreting visual information, they frequently produce content that deviates from visual…
One for All: Update Parameterized Knowledge Across Multiple Models
Weitao Ma, Xiyuan Du, Xiaocheng Feng +8
Large language models (LLMs) encode vast world knowledge but struggle to stay up-to-date, often leading to errors and hallucinations. Knowledge editing offers an efficient alternat…