Four Algoverse student research papers have been cited by leading academic and tech institutions in their AI-focused publications. This recognition demonstrates the meaningful impact young researchers can have on the field.
1. AAVENUE — Cited by Microsoft, Oxford, and University of Washington in "One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks" (arxiv.org/pdf/2410.11005). Focus: fairness and inclusivity in language models.
2. DiversityMedQA — Cited by NIH in "Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine" (arxiv.org/abs/2411.14487). Focus: reliability in AI medical applications.
3. Question-Analysis Prompting — Cited by Mount Sinai in "A Strategy for Cost-Effective Large Language Model Use at Health System-Scale" (nature.com/articles/s41746-024-01315-1). Focus: balancing AI costs in healthcare.
4. From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models — Cited in "GPT-4o Reads the Mind in the Eyes" (arxiv.org/pdf/2410.22309). Focus: interpreting mental states from facial expressions.
