A New Research from Google DeepMind Challenges the Effectiveness of Unsupervised Machine Learning Methods in Knowledge Elicitation from Large Language Models

Unsupervised methods fail to elicit knowledge as they genuinely prioritize prominent features. Arbitrary components conform to consistency structure. Improved evaluation criteria are needed. Persistent identification issues are anticipated in future unsupervised methods. Researchers from Google DeepMind and Google Research address issues in unsupervised knowledge discovery with LLMs, particularly focusing on methods utilizing probes trained on […]

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