Simple Search Algorithms on Semantic Networks Learned from Language Use
Bibliographic record
Abstract
Recent empirical and modeling research has focused on thesemantic fluency task because it is informative about seman-tic memory. An interesting interplay arises between the rich-ness of representations in semantic memory and the complex-ity of algorithms required to process it. It has remained anopen question whether representations of words and their re-lations learned from language use can enable a simple searchalgorithm to mimic the observed behavior in the fluency task.Here we show that it is plausible to learn rich representationsfrom naturalistic data for which a very simple search algorithm(a random walk) can replicate the human patterns. We sug-gest that explicitly structuring knowledge about words into asemantic network plays a crucial role in modeling human be-havior in memory search and retrieval; moreover, this is thecase across a range of semantic information sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".