Using Compositionality to Learn Many Categories from Few Examples
Bibliographic record
Abstract
Humans have the remarkable ability to learn new categories from few examples, but how few examples can we actually learn from? Recent studies suggest it may be possible to learn more novel concepts than the number of examples. Previous approaches to such less-than-one-shot (LO-shot) learning used soft labels to provide weighted mappings from each example to multiple categories. Unfortunately, people find soft labels unintuitive and this approach did not provide plausible, cognitively-grounded mechanisms for LO-shot learning at scale. We propose a new paradigm that leverages well-established learning strategies: reducing complex stimuli to primitives, learning by discrimination, and generalizing to novel compositions of features. We show that participants can learn 22 categories from just 4 examples, shedding light on the mechanisms involved in LO-shot learning. Our results provide valuable insights into the human ability to learn many categories from limited examples, and the strategies people employ to achieve this impressive feat.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".