Large Language Models
Work on making large language models useful when the stakes are real: choosing prompts, models, and embeddings at run time rather than fixing them in advance, building conversational agents that gather context before they advise, and measuring where frontier models still fall short. The thread running through it is that an LLM is a component in a system, not the system itself.
3 papers
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Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs
COLING 2025
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PATHFinder Agent for Tailored Prenatal Care
Interactive Health 2026
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RELIANCE: Curating and Evaluating Reproductive Health Information on Social Media
KDD 2026 · Datasets and Benchmarks Track