most citedLanguage Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SE2026

EigenData: A Self-Evolving Multi-Agent Platform for Function-Calling Data Synthesis, Auditing, and Repair

Jiaao Chen, Jingyuan Qi, Mingye Gao +3

Function-calling agents -- large language models that invoke tools and APIs -- require high-quality, domain-specific training data spanning executable environments, backing databas…

cs.CL2024★ 1 cited

The use of large language models to enhance cancer clinical trial educational materials

Mingye Gao, Aman Varshney, Shan Chen +15

Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the…

cs.CL2024

Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation

Shan Chen, Mingye Gao, Kuleen Sasse +6

Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong info…

cs.CL2024

Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias

Shan Chen, Jack Gallifant, Mingye Gao +12

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in t…

cs.CL2024★ 1 cited

Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks

Jack Gallifant, Shan Chen, Pedro Moreira +7

Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. This is particularly crucia…