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Whether for working co-ops far and wide or strengthening community close to home, Khoury College's students have made their mark. Per annual tradition, ten were recognized this spring for their achievements. Read more: https://bit.ly/4uP2MtI
This is a great follow-up to our recent preprint! This small-scale evaluation introduces a framing-resistant prompt and makes a step toward exploring the mitigation space for the framing sensitivity problem.
Thrilled to share our research showing how LLM models can be influenced by bias from "spun" medical literature is now featured in Northeastern's Khoury news! This shows critical insights as AI enters healthcare. The full paper can be found at arxiv.org/abs/2502.07963
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This framing effect is further amplified in multi-turn conversations, where sustained persuasion increases inconsistency. [3/6]
We also compared using technical terms vs plain language terms in our questions. However, we didn’t find any meaningful differences in this language style. [4/6]
Humans can be easily influenced by language that is one-sided, especially in complex fields like medicine. But a new Khoury-led study shows that large language models, too, can be tricked […]
khoury.northeastern.edu
As AI expands into medicine, Northeastern study finds AI models influenced by medical bias  - Khoury College of Computer Sciences
Patients ask LLMs medical questions — but how they phrase it matters more than it should. Our new preprint explores how different phrasings of patient health questions can lead to inconsistent conclusions, even with the same evidence. [1/6] Full Paper: arxiv.org/abs/2604.05051
Our conclusion: LLM medical responses vary based on question phrasing alone, despite identical underlying evidence. For patients and consumers, how you ask may determine what you're told. [5/6]