Search Results for author: Lizhou Fan

Found 12 papers, 6 papers with code

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis

1 code implementation23 Apr 2024 Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang

This novel system aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time.

Decision Making Language Modelling

Large Language Models in Biomedical and Health Informatics: A Bibliometric Review

no code implementations24 Mar 2024 Huizi Yu, Lizhou Fan, Lingyao Li, Jiayan Zhou, Zihui Ma, Lu Xian, Wenyue Hua, Sijia He, Mingyu Jin, Yongfeng Zhang, Ashvin Gandhi, Xin Ma

Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research.

Management Medical Diagnosis

Health-LLM: Personalized Retrieval-Augmented Disease Prediction System

no code implementations1 Feb 2024 Mingyu Jin, Qinkai Yu, Dong Shu, Chong Zhang, Lizhou Fan, Wenyue Hua, Suiyuan Zhu, Yanda Meng, Zhenting Wang, Mengnan Du, Yongfeng Zhang

Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction.

Disease Prediction Language Modelling +3

NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes

1 code implementation22 Dec 2023 Lizhou Fan, Wenyue Hua, Lingyao Li, Haoyang Ling, Yongfeng Zhang

Complex reasoning ability is one of the most important features of current LLMs, which has also been leveraged to play an integral role in complex decision-making tasks.

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

1 code implementation26 May 2023 Lizhou Fan, Sara Lafia, Lingyao Li, Fangyuan Yang, Libby Hemphill

Data users need relevant context and research expertise to effectively search for and identify relevant datasets.

Chatbot Language Modelling +1

"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media

1 code implementation20 Apr 2023 Lingyao Li, Lizhou Fan, Shubham Atreja, Libby Hemphill

To investigate this potential, we used ChatGPT and compared its performance with MTurker annotations for three frequently discussed concepts related to harmful content: Hateful, Offensive, and Toxic (HOT).

A Bibliometric Review of Large Language Models Research from 2017 to 2023

no code implementations3 Apr 2023 Lizhou Fan, Lingyao Li, Zihui Ma, Sanggyu Lee, Huizi Yu, Libby Hemphill

Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their ability to generate human-like language and their potential to revolutionize science and technology.


A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature

no code implementations23 May 2022 Sara Lafia, Lizhou Fan, Libby Hemphill

The pipeline increases recall for literature to review for inclusion in data-related collections of publications and makes it possible to detect informal data references at scale.

named-entity-recognition Named Entity Recognition +1

Librarian-in-the-Loop: A Natural Language Processing Paradigm for Detecting Informal Mentions of Research Data in Academic Literature

no code implementations10 Mar 2022 Lizhou Fan, Sara Lafia, David Bleckley, Elizabeth Moss, Andrea Thomer, Libby Hemphill

The librarian-in-the-loop paradigm is centered in the data work performed by ICPSR librarians, supporting broader efforts to build a more comprehensive bibliography of data-related literature that reflects the scholarly communities of research data users.

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