activity
20222024
most citedFrom Beginner to Expert: Modeling Medical Knowledge into General LLMs

4 citations · 9 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL20244 cited

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Qiang Li, Xiaoyan Yang, Haowen Wang +14

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However,…

cs.CL2023

EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval

Yiyao Yu, Junjie Wang, Yuxiang Zhang +3

Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conver…

cs.CL2023

UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective

Ping Yang, Junyu Lu, Ruyi Gan +4

We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition…

cs.CL20232 cited

NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension

Yuxiang Zhang, Junjie Wang, Xinyu Zhu +2

Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research…

cs.CL2022

Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss

Junjie Wang, Yuxiang Zhang, Ping Yang +1

This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge. In the pre-training stage, we construct…