1 citations · 1 across the 3 of their papers we have counts for
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Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
Skatje Myers, Dmitriy Dligach, Timothy A. Miller +6
Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health re…
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval
He Cheng, Yifu Wu, Saksham Khatwani +5
Knowledge graphs (KGs) are increasingly integrated with large language models (LLMs) to provide structured, verifiable reasoning. A core operation in this integration is multi-hop…
CLSGen: A Dual-Head Fine-Tuning Framework for Joint Probabilistic Classification and Verbalized Explanation
WonJin Yoon, Kangyu Zhu, Ian Bulovic +5
With the recent progress of Large Language Models (LLMs), there is a growing interest in applying these models to solve complex and challenging problems. Modern LLMs, capable of pr…
Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning
Saksham Khatwani, He Cheng, Majid Afshar +2
Large language models (LLMs) show promise for diagnostic reasoning but often lack reliable, knowledge grounded inference. Knowledge graphs (KGs), such as the Unified Medical Langua…
Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions
Ruizhe Li, Yanjun Gao
Large Language Models (LLMs), such as the GPT-4 and LLaMA families, have demonstrated considerable success across diverse tasks, including multiple-choice questions (MCQs). However…
Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study
Yanjun Gao, Ruizhe Li, Emma Croxford +6
Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. How…