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Master LLMs: Top Strategies to Evaluate LLM Performance
In this video, we look into how to evaluate and benchmark Large Language Models (LLMs) effectively. Learn about perplexity, other evaluation metrics, and curated benchmarks to compare LLM performance. Uncover practical tools and resources to select the right model for your specific needs and tasks. Dive deep into examples and comparisons to empower your AI journey!
â–º Jump on our free LLM course from the Gen AI 360 Foundational Model Certification (Built in collaboration with Activeloop, Towards AI, and the Intel Disruptor Initiative): https://learn.activeloop.ai/courses/llms/?utm_source=social&utm_medium=youtube&utm_campaign=llmcourse
â–ºMy Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/
With the great support of Cohere & Lambda.
â–º Course Official Discord: https://discord.gg/learnaitogether
â–º Activeloop Slack: https://slack.activeloop.ai/
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â–ºFollow me on Twitter: https://twitter.com/Whats_AI
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How to start in AI/ML - A Complete Guide:
â–ºhttps://www.louisbouchard.ai/learnai/
Become a member of the YouTube community, support my work and get a cool Discord role :
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Chapters:
0:00 Why and How to evaluate your LLMs!
0:50 The perplexity evaluation metric.
3:20 Benchmarks and leaderboards for comparing performances.
4:12 Benchmarks for Coding benchmarks.
5:33 Benchmarks for Reasoning and common sense.
6:32 Benchmark for mitigating hallucinations.
7:35 Conclusion.
#ai #languagemodels #llm
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