From the 1 of 12 linked papers with an AI index.
1 citations · 2 across the 6 of their papers we have counts for
8 papers · 1 filter
CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG
Lang Zhou, Yingjian Chen, Shuxuan Li +2
The paper proposes CMT-RAG, a framework that stores structured sub-question reasoning traces as memory to improve multi-turn, multi-hop retrieval‑augmented generation, and introduc…
Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
Xiaojie Gu, Sherry T. Tong, Aosong Feng +8
Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks witho…
From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
Yingjian Chen, Haoran Liu, Yinhong Liu +7
Large Language Models (LLMs) show strong reasoning ability in open-domain question answering, yet their reasoning processes are typically linear and often logically inconsistent. I…
Toward Global Large Language Models in Medicine
Rui Yang, Huitao Li, Weihao Xuan +47
Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed…
Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
Rui Yang, Matthew Yu Heng Wong, Huitao Li +13
The rapid growth of medical knowledge and increasing complexity of clinical practice pose challenges. In this context, large language models (LLMs) have demonstrated value; however…
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
Yingjian Chen, Haoran Liu, Yinhong Liu +8
Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such a…