5 papers · 1 filter
When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models
Yinfeng Wang, Zhiyuan Yao, Zheren Fu +2
Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored. In this paper, w…
MAVEN: A Macro-Societal Value Evaluation Framework of Multimodal Content with Compact Aligned Evaluators
Zijuan Zhao, Zheren Fu, Hou Xia +3
Assessing whether multimodal content aligns with macro-societal values, such as peace, justice, and freedom, has become an increasingly urgent challenge. Existing frameworks are la…
Mitigating Biases in Language Models via Bias Unlearning
Dianqing Liu, Yi Liu, Guoqing Jin +1
Many studies have shown various biases targeting different demographic groups in language models, amplifying discrimination and harming fairness. Recent parameter modification debi…
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models
Yi Liu, Dianqing Liu, Mingye Zhu +3
The widespread adoption of large language models (LLMs) across industries has increased the demand for high-quality and customizable outputs. However, traditional alignment methods…
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
Shaohan Wang, Licheng Zhang, Zheren Fu +2
Retrieval-Augmented Generation (RAG) is an effective method to enhance the capabilities of large language models (LLMs). Existing methods typically optimize the retriever or the ge…