2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Surgical Alignment in Knowledge Graph Training for Clinical Diagnosis with Large Language Models
Saksham Khatwani, He Cheng, Majid Afshar +2
Biomedical knowledge graphs (KGs) offer structured medical knowledge that can ground large language model (LLM) reasoning in clinical diagnosis application, yet how KG signal shoul…
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…
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
Emma Croxford, Yanjun Gao, Nicholas Pellegrino +7
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine r…