Practically Intelligent E4: Evaluating Large Language Models with Nathan Lambert
Aug 17, 2023
Sinan and Akshay chat with Nathan Lambert, a prominent Machine Learning researcher and analyst. They discuss evaluating language models, the Open LLM leaderboard, Luther's test harness, and the challenges of evaluating large language models and low data quality.
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LLM Evaluation Challenges
- Many evaluation tools exist for LLMs, but they feel disparate given the models' expanded use cases.
- This leads to false claims about model capabilities, making integration challenging.
Open LLM Leaderboard Evolution
- Hugging Face's Open LLM Leaderboard started as an internal tool but became a discovery tool.
- Subtle prompting differences, like basic context inclusion, significantly impact benchmark results.
Leaderboard Discoverability vs. Applicability
- The leaderboard's discoverability is a major advantage for those new to LLMs.
- It primarily focuses on reasoning tasks, making its applicability to other NLP tasks less clear.