With the proliferation of large language models (LLMs) like GPT-4 and others, understanding their strengths requires benchmarks. These benchmarks help assess different capabilities like academic knowledge, math reasoning, code generation, and language proficiency. While benchmarks are essential for cutting through marketing hype, they can be affected by training influences. Multimodal and multilingual tests are especially critical as they test real-world applicability. Leaderboards such as LMSYS and Hugging Face offer comparative insights based on these benchmarks.
Table of contents
Why We Even Need BenchmarksKey Benchmarks for Text ModelsHow Models Handle Other LanguagesVision Benchmarks: How Image-Ready Are These Models?Audio Benchmarks: Can They Listen?Where to Compare ModelsCommon Metrics (And What They Mean)Is the Model Actually Smart — Or Just Well-Trained?Final ThoughtsSources33 Impressions