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Towards fair and comprehensive multilingual LLM benchmarking

Cohere

Explore how to create fair, multilingual, and multicultural benchmarks for large language models, advancing inclusivity in AI research.

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Tiny Aya Expedition Drives Multilingual InnovationCohere How To Train Your Pet LLM: Prompt EngineeringCohere Cohere Transcribe Arabic: Open-Source Speech AICohere Turbocharge Your Chatbot with Intent RecognitionCohere Why You Need a Large Language Model for Intent RecognitionCohere Introducing: Cohere For AICohere Summer Data Engineering RoadmapMotherduck bunny.net expands into Nairobi, KenyaBunny How Walsall Council Delivers Improved Resident Experiences Through Multilingual AI Videos in Over 50Synthesia Product updates: Running batch jobs with 1M inputs, ephemeral apps, and a new TensorRT-LLM exampleModal AI-generated Awareness Campaigns: Global ImpactHeygen Flux Multilingual Technical Deep Dive: Multilingual Speech-to-Text Without the Routing MessDeepgram How Cision uses AI video to scale multilingual support and improve client experienceSynthesia How to Evaluate an SCA with Reachability: Benchmarking Hard-to-Analyze Language FeaturesSocket Documentation - JS Projects Utilizing TypeScriptTypescriptlang Developer Experience: Devs Shouldn't Have to "Figure It Out"Thenewstack LLM-driven data pipelines with prompt() in MotherDuck and dbtMotherduck Extending LLM context with 99% less training tokens - CerebrasCerebras Cerebras and G42 Complete 4 exaFLOP AI Supercomputer and Start the March Towards 8 exaFLOPsCerebras Cerebras and Qualcomm Unleash ~10X Inference Performance Boost with Hardware-Aware LLM TrainingCerebras