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Postingan yang diterbitkan di instalasi FPDP ini.

Behavioral history outperforms descriptions of the person for LLM synthetic personas. A respondent's past answers add more to individual-level prediction than a description of who they are.
Source: arXiv
https://arxiv.org/abs/2610.03998
#MachineLearning
Sparse Autoencoders decompose model activations into sparse combinations of interpretable dictionary atoms. Their objective smuggles in an additional prior where concepts across patches are treated as independent. Spatial-SAE is proposed as an amortized MAP estimator.
Source: arXiv
https://arxiv.org/abs/2610.04112
#MachineLearning
Researchers measured how well one expert human reader and three LLM families reproduced their own reading of Sumerian myths over time. Across runs months apart, the human cut the text in much the same places but named the segments differently.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04370
#MachineLearning
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