SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language
Bibliographic record
Abstract
Recently, the idea of comparison of models’ representations and human brain signals has been a topic of several works. Consequently, several datasets with text data and EEG representations have been published. However, most of the datasets are based on normal reading task with grammatical sentences. At the same time, in the interpretability studies of LLMs, more and more attention is paid to thoroughly designed linguistic tasks based on acceptability measures. In this paper, we present SIGNAL, a dataset for Semantic and Inferred Grammar Neurological Analysis of Language. Our dataset contains a group of sentences with a combination of a fully acceptable sentence and a grammatically or/and semantically incongruent sentences. The dataset has been approved by native speakers and later used for an EEG experiment. In total, our dataset contains recordings of 21 participants, each of whom read 600 sentences. In addition, we present a pilot study where we compare EEG analysis with simple probing experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".