28 citations · 108 across the 31 of their papers we have counts for
4 papers · 1 filter
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction
Chang Su, Dengliang Shi, Siyuan Huang +5
Large language models (LLMs) have increasingly been explored as powerful text embedders. Existing LLM-based text embedding approaches often leverage the embedding of the final toke…
Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Yuqin Dai, Shuo Yang, Guoqing Wang +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges.…
Agent Safety Alignment via Reinforcement Learning
Zeyang Sha, Hanling Tian, Zhuoer Xu +3
The emergence of autonomous Large Language Model (LLM) agents capable of tool usage has introduced new safety risks that go beyond traditional conversational misuse. These agents,…
Gumbel Reranking: Differentiable End-to-End Reranker Optimization
Siyuan Huang, Zhiyuan Ma, Jintao Du +5
RAG systems rely on rerankers to identify relevant documents. However, fine-tuning these models remains challenging due to the scarcity of annotated query-document pairs. Existing…