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#representations

2 curated events
papersTODAY 04:00 UTC

Study finds demographic identity in language models splits into three distinct properties

A new arXiv paper argues that a language model's demographic identity has three separable aspects — whether it is readable, whether it is faithful to real group differences, and whether the model actually uses it when generating text. The authors test this using internal representations, addressing why LLM-simulated survey respondents tend to be homogeneous and diverge from real inter-group patterns. The work suggests unfaithful simulation may stem from what models use rather than what they know.

research/llm-evaluation/survey-simulation/representations
papersSEP 10 04:00 UTC

Researchers probe whether speech foundation models truly learn words

A new arXiv study investigates self-supervised speech foundation models, which are widely deployed for speech recognition and to supply tokens for speech-capable language models. The authors analyze what these models' internal representations encode, testing whether they capture genuine word-level linguistic structure rather than only acoustic patterns. The results bear on how such models should be interpreted and used in downstream speech applications.

speech/foundation-models/interpretability/representations