4 citations · 4 across the 7 of their papers we have counts for
7 papers
Neu-RadBERT for Enhanced Diagnosis of Brain Injuries and Conditions
Manpreet Singh, Sean Macrae, Pierre-Marc Williams +6
Objective: We sought to develop a classification algorithm to extract diagnoses from free-text radiology reports of brain imaging performed in patients with acute respiratory failu…
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Suyuchen Wang, Jinlin Wang, Xinyu Wang +6
Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches ei…
GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks
Hao Xu, Xiangru Jian, Xinjian Zhao +9
This paper introduces GraphOmni, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs on graph-theoretic tasks articulated in natural language. GraphOm…
FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval
Jinlin Wang, Suyuchen Wang, Ziwen Xia +4
Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts,…
Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration
Jeremy Qin, Bang Liu, Quoc Dinh Nguyen
Black-box large language models (LLMs) are increasingly deployed in various environments, making it essential for these models to effectively convey their confidence and uncertaint…
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection
Yun Zhu, Jia-Chen Gu, Caitlin Sikora +8
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linea…
Can Github issues be solved with Tree Of Thoughts?
Ricardo La Rosa, Corey Hulse, Bangdi Liu
While there have been extensive studies in code generation by large language models (LLM), where benchmarks like HumanEval have been surpassed with an impressive 96.3% success rate…