Combining independent modules to solve multiple-choice synonym and analogy problems\n
Bibliographic record
Abstract
Existing statistical approaches to natural language problems are very \ncoarse approximations to the true complexity of language processing.\nAs such, no single technique will be best for all problem instances. \nMany researchers are examining ensemble methods that combine the\noutput of successful, separately developed modules to create more \naccurate solutions. This paper examines three merging rules for \ncombining probability distributions: the well known mixture rule, the \nlogarithmic rule, and a novel product rule. These rules were applied \nwith state-of-the-art results to two problems commonly used to assess \nhuman mastery of lexical semantics -- synonym questions and analogy\nquestions. All three merging rules result in ensembles that are more \naccurate than any of their component modules. The differences among the\nthree rules are not statistically significant, but it is suggestive \nthat the popular mixture rule is not the best rule for either of the \ntwo problems.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".