The Biggest Mistake Tech Companies Are Making With AI Is Choosing Models Based on Hype, Not True...

This comprehensive guide outlines various benchmarks used to evaluate large language models (LLMs) across multiple dimensions, including capability, alignment, and safety. Here's a summary of key categories and specific benchmarks mentioned:

Capability Benchmarks

1. Mathematics

   • AIME 2024/2025: Deep mathematical reasoning.
   • MATH/MATH-500: Competition math problems.
   • FrontierMath: Cutting-edge research-level mathematics.
   • OlympiadBench: International Math Olympiad level.

2. Agentic and Tool Use

   • (No specific benchmarks listed, but implied importance for multi-step real-world tasks)

3. Long Context

   • Benchmarks assessing performance with large amounts of context data (100K-1M tokens).

4. Vision and Multimodal

   • NOVA: Visual reasoning.
   • MMCLIP: Multimodal understanding.

5. Chat Quality and Instruction Following

   • Chatbot Arena/LMArena: Human preference ELO ratings.
   • IFBench: Hard instruction following.
   • IFEval: Strict format constraints.
   • MT-Bench/Arena-Hard: Multi-turn conversation quality

Read the full article at Towards AI - Medium

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