Titlebook: Health Information Processing. Evaluation Track Papers; 9th China Conference Hua Xu,Qingcai Chen,Hui Zong Conference proceedings 2024 The E

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簡略

Conference proceedings 2024angzhou, China, during October 27–29, 2023.?The 15 algorithms papers and 6 overview papers included in this book were carefully reviewed and selected from a total of 66 submissions to the conference. They were organized in topical sections as follows: CHIP-PromptCBLUE Medical Large Model Evaluation;

Monolithic

Overview of?the?PromptCBLUE Shared Task in?CHIP2023, and provide a good testbed for Chinese open-domain or medical-domain large language models (LLMs) in general medical natural language processing. Two different tracks are held: (a) prompt tuning track, investigating the multitask prompt tuning of LLMs, (b) probing the in-context learning capabilit

isotope

羽飾

CMed-Baichuan: Task Explanation-Enhanced Prompt Method on?PromptCBLUE Benchmarkowledge-intensive nature of the medical field, previous studies proposed various fine-tuning methods and fine-tuned domain LLMs to align the general LLMs into specific domains. However, they ignored the difficulty of understanding the medical task requirements, that is LLMs are expected to give answ

convert

Improving LLM-Based Health Information Extraction with In-Context Learningansformed into prompt based language generation tasks. On the other hand, LLM can also achieve superior results on brand new tasks without fine-tuning, solely with a few in-context examples. This paper describes our participation in the China Health Information Processing Conference (CHIP 2023). We

Chemotherapy

漂白

Similarity-Based Prompt Construction for Large Language Model in Medical Taskstential of using LLM to unify diverse NLP tasks into a text generative manner. In order to explore the potential of LLM for In-Context Learning in Chinese Medical field, the 9th China Health Information Processing Conference (CHIP 2023) has released a non-tuning LLM evaluation task called PromptCBLU

Compass

CMF-NERD: Chinese Medical Few-Shot Named Entity Recognition Dataset with?State-of-the-Art Evaluationical NER datasets available. The difficulty to share private data and varying specifications presented pose a challenge to this research. In this paper, We merged and cleaned multiple sources of Chinese medical NER dataset, then restructured these data into few-shot settings. CMF-NERD was constructe