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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".