55 citations · 55 across the 5 of their papers we have counts for
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cs.CL2024
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
Xiangsen Chen, Xuming Hu, Nan Tang
Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incor…
cs.CL2024
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models
Boyang Xue, Fei Mi, Qi Zhu +6
Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often struggle to accurately express the factual knowledge they possess, especially in cases where…
cs.CL2024
Reasoning Factual Knowledge in Structured Data with Large Language Models
Sirui Huang, Yanggan Gu, Xuming Hu +3
Large language models (LLMs) have made remarkable progress in various natural language processing tasks as a benefit of their capability to comprehend and reason with factual knowl…