6 citations · 14 across the 8 of their papers we have counts for
5 papers · 1 filter
Mitigating Entity-Level Hallucination in Large Language Models
Weihang Su, Yichen Tang, Qingyao Ai +3
The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions w…
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Haitao Li, Qingyao Ai, Jia Chen +5
Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data…
DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models
Weihang Su, Yichen Tang, Qingyao Ai +2
Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two k…
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
Weihang Su, Changyue Wang, Qingyao Ai +4
Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectivene…
Understanding the Effect of Opinion Polarization in Short Video Browsing
Bangde Du, Ziyi Ye, Monika Jankowska +3
This paper explores the impact of Opinion Polarization (OP) in the increasingly prevalent context of short video browsing, a dominant medium in the contemporary digital landscape w…