concept
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concept:connecting-the-dots-llms-can-infer-and-verbalize-latent-structure-from-disparate-training-data-treutlein-et-al-2024Connecting the Dots: LLMs Can Infer and Verbalize Latent Structure from Disparate Training Data (Treutlein et al. 2024)
Out-of-context reasoning work directly related to synthetic document fine-tuning experiments
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Papers (1)
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- Johannes Treutleinauthored
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Supporting evidence for cross-modal platonic representation
- Sharma et al. result supporting cross-modal alignment: language-only models implicitly encode visual structure
- Establishes that the observed linear structure is not merely a representation of text probability
- Key limitation and open question about experimental scope.
- Binder et al. finding cited as evidence that LLMs possess introspective capacity analogous to mindfulness
- Understanding how LMs learn linguistic behaviours may offer insights into fundamental properties of languagehypothesis0.801Forward-looking hypothesis linking LM mechanism analysis to linguistic theory
- Skeptical prior work motivating the need to validate self-reports against internal states rather than taking them at face value
- Qualified positive claim from spatio permutation analysis where two cases satisfy all three criteria.