5 papers
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…
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Maya Kruse, Majid Afshar, Saksham Khatwani +3
Large language models (LLMs) often behave inconsistently across inputs, indicating uncertainty and motivating the need for its quantification in high-stakes settings. Prior work on…
Development and Validation of the Provider Documentation Summarization Quality Instrument for Large Language Models
Emma Croxford, Yanjun Gao, Nicholas Pellegrino +16
As Large Language Models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementatio…