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Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement

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Finetuning large language models on instruction data is crucial for enhancing pre-trained knowledge and improving instruction-following capabilities.

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Research Cohere

ResearchMetadata Archaeology: Unearthing Data Subsets by Leveraging Training DynamicsCohere ResearchBridging the Data Provenance Gap Across Text, Speech, and VideoCohere ResearchScalable Data Ablation Approximations for Language Models through Modular Training and MergingCohere ResearchSEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian LanguagesCohere ResearchPolicy Primer - Efficient AICohere ResearchUnderstanding and Mitigating Language Confusion in LLMsCohere EventsCloud Security Trends & Challenges: Complete GuideCybersecurity Exchange BlogsState of Speech: Our New Data Report Reveals ASR’s Untapped Potential - Deepgram Blog ⚡️Deepgram LearnBuild a Presentation Coaching Application with Recall - Deepgram Blog ⚡️Deepgram BlogsThe Most Important Work in AI Training Is Also the Most Overlooked - Deepgram Blog ⚡️Deepgram BlogsData Ingestion: 6 Ways to Speed Up Your ApplicationRedis Blogs5 ways to automate Browse AIZapier BlogsHow to connect Google Sheets to NotionZapier NewsPancakes Are Delicious and Data Centers Are for Free StuffThenewstack NewsExplore and Visualize Data the Apache Superset WayThenewstack BlogsGo Concurrency Patterns: Pipelines and cancellation - The Go Programming LanguageGo Products & ServicesData connections - FeaturesJetbrains BlogsHow a top tier European soccer team sped up their data processing and reduced costs by 50%Modal ResourcesWhat is a Load/Store Unit?Modal BlogsAnthropic integration with Modal brings scalable compute to Claude ScienceModal