Researchers from Google DeepMind and Google Research analyze the limitations of current unsupervised methods in discovering latent knowledge within large language models (LLMs). They question the specificity of the CCS method and propose sanity checks for evaluating plans, emphasizing the need for improved unsupervised approaches to address persistent identification issues. Read the full paper for in-depth details.
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Google Research: Challenges in Unsupervised Machine Learning for Knowledge Discovery
Summary
The study by researchers from Google DeepMind and Google Research evaluates the limitations of current unsupervised methods to discover latent knowledge in Large Language Models (LLMs). It doubts the specificity of the contrast-consistent search (CCS) method and proposes sanity checks and improved unsupervised approaches.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- A New Research from Google DeepMind Challenges the Effectiveness of Unsupervised Machine Learning Methods in Knowledge Elicitation from Large Language Models
- MarkTechPost
- Twitter – @itinaicom
