LLM Evaluation and Benchmarks Deep Dive

Systematic understanding of LLM evaluation: from benchmark design principles to specific benchmark deep dives, from optimization accuracy assessment to model selection decisions. Covers knowledge, reasoning, code, and agent evaluation with focus on OpenVINO toolchain and small model assessment.

1. 1

   Benchmark Landscape and Evaluation Methodology

   Intermediate

   #benchmark#evaluation#methodology#llm-as-judge#contamination
2. 2

   Knowledge & Reasoning Benchmarks

   Intermediate

   #benchmark#reasoning#mmlu#gpqa#math
3. 3

   Code Benchmarks

   Intermediate

   #benchmark#code#humaneval#swe-bench#pass-at-k
4. 4

   Agent & Tool Use Benchmarks

   Intermediate

   #benchmark#agent#function-calling#tool-use#bfcl#gaia
5. 5

   Anatomy of Model Release Benchmark Standard Sets

   Intermediate

   #benchmark#model-release#standard-set#small-models#gemma#phi#qwen
6. 6

   Impact of Optimization on Accuracy

   Intermediate

   #benchmark#quantization#accuracy#perplexity#openvino#lm-eval-harness#llama-cpp
7. 7

   Interpreting Leaderboards and Model Selection

   Intermediate

   #benchmark#leaderboard#model-selection#chatbot-arena#deployment
8. 8

   lm-eval-harness Practical Guide

   Advanced

   #benchmark#lm-eval#evaluation#harness#task-yaml
9. 9

   SWE-bench Practical Guide

   Advanced

   #benchmark#swe-bench#code-evaluation#agent#docker
10. 10

    BFCL Practical Guide

    Advanced

    #benchmark#bfcl#function-calling#tool-use#evaluation