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arXiv:2505.12196v2 Announce Type: replace Abstract: The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conject...
The paper investigates the intrinsic dimension (ID) of large language model (LLM) representations as an indicator of linguistic complexity. By comparing ID across model layers for coordination vs. subordination, right‑branching vs. center‑embedding, and unambiguous vs. ambiguous attachment, the authors find consistent ID differences that align with established complexity contrasts. Experiments across six LLMs, including representational similarity and layer pruning analyses, confirm that more complex phenomena produce higher ID profiles, with peaks occurring at different layers for each contrast.
The study investigates whether large language models (LLMs) can generate mentalization profiles that are structurally coherent with mentalization‑based treatment (MBT) dimensions. Fifty GPT‑4.1 dialogues were produced, and the resulting narrative profiles were rated by five MBT‑trained psychiatrists on evaluative and argumentative coherence across canonical MBT axes and higher‑order dimensions. Ratings showed moderate‑to‑high structural coherence (mean scores 3.63–3.98) with strong interrater reliability (ICC(3,k) 0.60–0.84) and a strong association between evaluative and argumentative coherence (Pearson r 0.826–0.967). A negative control confirmed the specificity of the ratings, and clinicians noted that the outputs were linguistically organized and clinically interpretable, though lacking affective variability and contextual integration.
arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.
arXiv:2607.21574v2 Announce Type: replace Abstract: Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some langu...
The study examined whether brain-language model alignment reflects shared computational mechanisms or merely stable lexical‑semantic correspondences. Using whole‑brain encoding across Mandarin, English, and French, transformer representations predicted activity in a distributed network that overlapped across languages and remained stable across layers. Contextual embeddings and measures of prediction or compression did not outperform static lexical embeddings, suggesting that alignment is robust but not informative about shared computational processes.