3 citations · 4 across the 4 of their papers we have counts for
4 papers
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment
Kun Luo, Minghao Qin, Zheng Liu +3
Pretrained language models like BERT and T5 serve as crucial backbone encoders for dense retrieval. However, these models often exhibit limited generalization capabilities and face…
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models
Hongbang Yuan, Yubo Chen, Pengfei Cao +3
Large language models (LLMs) have achieved remarkable success but still tend to generate factually erroneous responses, a phenomenon known as hallucination. A recent trend is to us…
Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models
Zhuoran Jin, Pengfei Cao, Yubo Chen +5
Retrieval-augmented language models (RALMs) have demonstrated significant potential in refining and expanding their internal memory by retrieving evidence from external sources. Ho…
Prompting Vision Language Model with Knowledge from Large Language Model for Knowledge-Based VQA
Yang Zhou, Pengfei Cao, Yubo Chen +2
Knowledge-based visual question answering is a very challenging and widely concerned task. Previous methods adopts the implicit knowledge in large language models (LLM) to achieve…