2 papers
cs.CL2025
Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
Jonathan Liu, Haoling Qiu, Jonathan Lasko +3
Recent research has shown that hallucinations, omissions, and biases are prevalent in everyday use-cases of LLMs. However, chatbots used in medical contexts must provide consistent…
cs.IR2024
QueryBuilder: Human-in-the-Loop Query Development for Information Retrieval
Hemanth Kandula, Damianos Karakos, Haoling Qiu +4
Frequently, users of an Information Retrieval (IR) system start with an overarching information need (a.k.a., an analytic task) and proceed to define finer-grained queries covering…