most citedLarge Language Models as Source Planner for Personalized Knowledge-grounded Dialogue

1 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2024

MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models

Boyang Xue, Hongru Wang, Rui Wang +5

The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…

cs.CL20241 cited

UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval

Hongru Wang, Boyang Xue, Baohang Zhou +5

Conversational retrieval refers to an information retrieval system that operates in an iterative and interactive manner, requiring the retrieval of various external resources, such…

cs.CL2024

MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models

Boyang Xue, Hongru Wang, Rui Wang +6

The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…

cs.CL2023

Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment

Boyang Xue, Weichao Wang, Hongru Wang +7

Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In s…

cs.CL20231 cited

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue

Hongru Wang, Minda Hu, Yang Deng +7

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue sys…

cs.CL2023

SELF: Self-Evolution with Language Feedback

Jianqiao Lu, Wanjun Zhong, Wenyong Huang +9

Large Language Models (LLMs) have demonstrated remarkable versatility across various domains. To further advance LLMs, we propose 'SELF' (Self-Evolution with Language Feedback), a…