most citedChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

56 citations · 195 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023★ 56 cited

ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Chi-Min Chan, Weize Chen, Yusheng Su +5

Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have…

cs.AI2023★ 13 cited

ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory

Chenxu Hu, Jie Fu, Chenzhuang Du +3

Large language models (LLMs) with memory are computationally universal. However, mainstream LLMs are not taking full advantage of memory, and the designs are heavily influenced by…

cs.CL2023★ 54 cited

Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Miao Xiong, Zhiyuan Hu, Xinyang Lu +4

Empowering large language models to accurately express confidence in their answers is essential for trustworthy decision-making. Previous confidence elicitation methods, which prim…

cs.CL2023★ 13 cited

Huatuo-26M, a Large-scale Chinese Medical QA Dataset

Jianquan Li, Xidong Wang, Xiangbo Wu +6

In this paper, we release a largest ever medical Question Answering (QA) dataset with 26 million QA pairs. We benchmark many existing approaches in our dataset in terms of both ret…

cs.CL2023★ 30 cited

Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias

Zhongwei Wan, Che Liu, Mi Zhang +6

The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from variou…

cs.LG2023★ 14 cited

Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility

Wentao Ye, Mingfeng Ou, Tianyi Li +8

The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-s…

cs.LG2023★ 15 cited

MUDiff: Unified Diffusion for Complete Molecule Generation

Chenqing Hua, Sitao Luan, Minkai Xu +4

Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing me…