1.5k citations · 1.8k across the 22 of their papers we have counts for
5 papers · 1 filter
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Jinhao Jiang, Jiayi Chen, Junyi Li +5
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and t…
Self-Calibrated Listwise Reranking with Large Language Models
Ruiyang Ren, Yuhao Wang, Kun Zhou +5
Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passa…
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Ruiyang Ren, Peng Qiu, Yingqi Qu +5
Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e…
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
Yuhao Wang, Ruiyang Ren, Junyi Li +3
Considering the limited internal parametric knowledge, retrieval-augmented generation (RAG) has been widely used to extend the knowledge scope of large language models (LLMs). Desp…
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
Junyi Li, Jie Chen, Ruiyang Ren +4
In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment…