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Meta’s Self-Taught Evaluator enables LLMs to create their own training data
Researchers at Meta have released Self-Taught Evaluator, a technique that enables LLMs to automatically label their training data.
Human evaluation has been the gold standard for assessing the quality and accuracy of large language models (LLMs), especially for open-ended tasks such as creative writing and coding. These techniques can significantly reduce the manual effort required to create high-performing LLMs, paving the way for more efficient and scalable development and deployment of AI-powered applications. At the same time, fully automated loops that rely solely on LLMs to self-evaluate their own outputs can fall on meaningless shortcuts that optimize the model for a benchmark but fail on real-world tasks.
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