BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model

Research

The Big Science Workshop was a value-driven initiative that spanned one and half years of interdisciplinary research and culminated in the creation of ROOTS, a 1.6 TB multilingual dataset that was used to train BLOOM, one of the largest multilingual language models to date.

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ResearchProcedural Knowledge in Pretraining Drives Reasoning in Large Language ModelsCohere ResearchFishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language ModelsCohere ResearchRewardBench 2: Advancing Reward Model EvaluationCohere ResearchThe Multilingual Divide and Its Impact on Global AI SafetyCohere ResearchHere's a Free Lunch: Sanitizing Backdoored Models with Model MergeCohere ResearchLanguage Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-ThoughtCohere BlogsDeepSeek V3 just got vision capabilities!Fireworks BlogsVision Model Platform Updates: Enhanced Capabilities and New FeaturesFireworks BlogsWhy do all LLMs need structured output modes?Fireworks BlogsIntroducing FireRouter with OpusFireworks BlogsIntroducing Fireworks on Microsoft Foundry: Bringing Best-in-Class Open Model inference to AzureFireworks BlogsAccelerate your Vision Pipelines with the new NVIDIA Nemotron Nano 2 VL Model on FireworksFireworks BlogsIntroducing Supervised Fine-tuning V2Fireworks BlogsBeyond Supervised Fine Tuning: How Reinforcement Learning Empowers AI with Minimal LabelsFireworks

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