6 citations · 33 across the 50 of their papers we have counts for
10 papers · 1 filter
Knowledge Editing through Chain-of-Thought
Changyue Wang, Weihang Su, Qingyao Ai +2
Knowledge Editing is a technique that updates large language models (LLMs) with new information to maintain their world knowledge. This approach avoids the need to rebuild the mode…
Auto-PRE: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation
Junjie Chen, Weihang Su, Zhumin Chu +9
The rapid development of large language models (LLMs) has highlighted the need for efficient and reliable methods to evaluate their performance. Traditional evaluation methods ofte…
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…
LeKUBE: A Legal Knowledge Update BEnchmark
Changyue Wang, Weihang Su, Hu Yiran +6
Recent advances in Large Language Models (LLMs) have significantly shaped the applications of AI in multiple fields, including the studies of legal intelligence. Trained on extensi…
STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals
Weihang Su, Yiran Hu, Anzhe Xie +6
Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated jud…
Scaling Laws For Dense Retrieval
Yan Fang, Jingtao Zhan, Qingyao Ai +4
Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neu…
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…
Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions
Weihang Su, Qingyao Ai, Yueyue Wu +5
Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments.…
Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval
Weihang Su, Qingyao Ai, Xiangsheng Li +4
With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Be…