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most citedLarge Language Models Need Holistically Thought in Medical Conversational QA

5 citations · 10 across the 11 of their papers we have counts for

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cs.CL20235 cited

Large Language Models Need Holistically Thought in Medical Conversational QA

Yixuan Weng, Bin Li, Fei Xia +5

The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the succes…

cs.CL20231 cited

Heterogeneous-Branch Collaborative Learning for Dialogue Generation

Yiwei Li, Shaoxiong Feng, Bin Sun +1

With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. One promising approach to obtaining a high-…

cs.CL2022

Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation

Bin Li, Yixuan Weng, Ziyu Ma +2

This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided…

cs.CL20211 cited

SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation

Bin Li, Fei Xia, Yixuan Weng +2

Acronym disambiguation means finding the correct meaning of an ambiguous acronym from the dictionary in a given sentence, which is one of the key points for scientific document und…

cs.CL20212 cited

PSG: Prompt-based Sequence Generation for Acronym Extraction

Bin Li, Fei Xia, Yixuan Weng +3

Acronym extraction aims to find acronyms (i.e., short-forms) and their meanings (i.e., long-forms) from the documents, which is important for scientific document understanding (SDU…