21 citations · 37 across the 19 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.CL2023★ 6 cited
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
Haoqiang Kang, Xiao-Yang Liu
The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Despite th…
cs.CL2023★ 5 cited
Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
Haoqiang Kang, Juntong Ni, Huaxiu Yao
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating fluent text. However, they often encounter the challenge of generating inaccurate or hallucinate…
cs.CL2023
Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models
Haoqiang Kang, Terra Blevins, Luke Zettlemoyer
Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be finetuned to perform well on diverse tasks such as translation and multilingual word sense disambigu…