Yao Ge

MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering

Rezarta Islamaj, Robert Leaman, Joey Chan, Nicholas Wan et autres

Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabilities improve. Existing biomedical question answering (QA) benchmarks are limited in this respect. Multiple-choice formats can allow models to …

MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering

Rezarta Islamaj, Robert Leaman, Joey Chan, Nicholas Wan et autres

Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabilities improve. Existing biomedical question answering (QA) benchmarks are limited in this respect. Multiple-choice formats can allow models to …

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Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation

Yao Ge, Yuting Guo, Sudeshna Das, Abeed Sarker

Abstract Biomedical named entity recognition (NER) is a high-utility natural language processing task, and large language models (LLMs) show promise in few-shot settings. In this article, we address performance challenges for few-shot biomedical NER by investigating innovative prompting strategies involving retrieval-augmented generation. …

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GAT3D: Attention-Based Graph Neural Network for Efficient 3D Object Detection

Xun Zhang, Jie Tang, Yong Wan, Yao Ge

This paper proposes a 3D object detection method using graph neural networks to process LiDAR point clouds. The raw data is voxel-sampled into a multi-layer graph, with each node representing local geometric features. A graph attention mechanism (GAT) is employed to adaptively …

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Leveraging few-shot learning and large language models for analyzing blood pressure variations across biological sex from scientific literature

Yuting Guo, Seyedeh Somayyeh Mousavi, Yao Ge, Madhumita Baskaran et autres

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Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models

Yao Ge, Sudeshna Das, Yuting Guo, Abeed Sarker

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Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models

Yao Ge, Sudeshna Das, Yuting Guo, Abeed Sarker

Biomedical named entity recognition (NER) is a high-utility natural language processing (NLP) task, and large language models (LLMs) show promise particularly in few-shot settings (i.e., limited training data). In this article, we address the performance challenges of LLMs for few-shot biomedical NER …

Some Efficient Stock Price Trend Prediction Based on Multi-category Textual Information and Support Vector Machines

Yuncheng He, Yao Ge, Yanhong Gu

For a selected portfolio of large-cap blue-chip stocks in China A-share market, this study selects and quantifies three categories of textual information with comparatively notably low average daily volume: responses from Secretaries of the Boards of Listed Companies (RSB), Comments by Internet …

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HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models

Yao Ge, Yuting Guo, Sudeshna Das, Swati Rajwal et autres

We present HILGEN, a Hierarchically-Informed Data Generation approach that combines domain knowledge from the Unified Medical Language System (UMLS) with synthetic data generated by large language models (LLMs), specifically GPT-3.5. Our approach leverages UMLS's hierarchical structure to expand training data with related …

Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study

Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal et autres

Background The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, …

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“I Been Taking Adderall Mixing it With Lean, Hope I Don’t Wake Up Out My Sleep”: Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers

Joni-Leigh Webster, Sahithi Lakamana, Yao Ge, Abeed Sarker

Black women and men outpace other races for stimulant-involved overdose mortality despite lower lifetime use. Growth in mortality from prescription stimulant medications is increasing in tandem with prescribing patterns for these medications. We used Twitter to explore nonmedical prescription stimulant use (NMPSU) …

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Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study (Preprint)

Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal et autres

BACKGROUND The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, …