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Arxiv
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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
- A new study introduces a causal representation learning framework to evaluate language model capabilities effectively.
- The framework models benchmark performance as a linear transformation of a few latent capability factors.
- The latent factors are identified as causally interrelated after controlling for the base model as a common confounder.
- The study analyzed over 1500 models across six benchmarks and identified a concise three-node linear causal structure explaining performance variations.
- The causal structure revealed insights starting from general problem-solving capabilities, through instruction-following proficiency, to mathematical reasoning ability.
- The results emphasize the importance of controlling base model variations during evaluation to accurately uncover causal relationships among latent model capabilities.
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