Can open-source large language models really outperform closed ones like Claude 3.5? 🤔In this episode of the Women in AI Research podcast, Jekaterina Novikova and Malikeh Ehghaghi engage with Valentina Pyatkin, a postdoctoral researcher at the Allen Institute for AI. We dive deep into the future of open science, LLM research, and extending model capabilities.🔑 Topics we cover:Why open-source LLMs sometimes beat closed modelsThe value of releasing datasets, recipes, and training infrastructureThe role of open science in accelerating NLP innovationInsights from Valentina’s award-winning research journeyREFERENCES:⁠Valentina's Google Scholar profile ⁠⁠Olmo: Accelerating the science of language models⁠⁠Tulu 3: Pushing Frontiers in Open Language Model Post-Training⁠⁠open-instruct⁠⁠Generalizing Verifiable Instruction Following⁠⁠RewardBench 2: Advancing Reward Model Evaluation⁠🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.⁠WiAIR website⁠⁠LinkedIn⁠⁠Bluesky⁠⁠X (Twitter)

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