2 citations · 3 across the 4 of their papers we have counts for
11 papers · 1 filter
AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
Ran Xu, Yuchen Zhuang, Zihan Dong +7
Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-p…
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
Ran Xu, Yuchen Zhuang, Yue Yu +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved at inference time. While RAG demonstrates strong performance…
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
Ran Xu, Wenqi Shi, Yuchen Zhuang +4
Retrieval-Augmented Generation (RAG) systems often struggle to handle multi-hop question-answering tasks accurately due to irrelevant context retrieval and limited complex reasonin…
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
Tianci Liu, Haoxiang Jiang, Tianze Wang +5
Large language models (LLMs) have achieved impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. In cont…
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Wenqi Shi, Ran Xu, Yuchen Zhuang +5
Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and co…
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Ran Xu, Wenqi Shi, Yue Yu +6
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient public…