Bibliographic record
Abstract
In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching<br> (PPM) compression algorithm. This achieves significantly better compression for different natural<br> language texts compared to other well-known compression methods. Our method first generates a grammar<br> based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in<br> the text being compressed and then substitutes these sequences using the respective non-terminal symbols defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly improved results in compression for various natural languages (a 5% improvement for American English, 10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We describe further improvements using a two pass scheme where the grammar-based pre-processing is applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary Corpus and also achieve significantly improved results in compression, between 11% and 20%, when compared with other compression algorithms, including a grammar-based approach, the Sequitur algorithm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".