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Few-Shot Preference Optimization (FSPO): A Novel Machine Learning Framework Designed to Model Diverse Sub-Populations in Preference Datasets to Elicit Personalization in Language Models for Open-Ended Question Answering

Personalizing LLMs is essential for applications such as virtual assistants and content recommendations, ensuring responses align with individual user preferences. Unlike traditional approaches that optimize models based on aggregated user feedback, personalization aims to capture the diversity of individual perspectives…

Researchers from FutureHouse and ScienceMachine Introduce BixBench: A Benchmark Designed to Evaluate AI Agents on Real-World Bioinformatics Task

Modern bioinformatics research is characterized by the constant emergence of complex data sources and analytical challenges. Researchers routinely confront tasks that require the synthesis of diverse datasets, the execution of iterative analyses, and the interpretation of subtle biological signals. High-throughput…