Evaluation of Stability and Similarity of Latent Dirichlet Allocation
Bibliographic record
Abstract
Latent Dirichlet Allocation (LDA) is an unsupervised, statistical method to model documents and discover latent semantic topics from large set of documents and categorize them into learned topics. In this paper, we first introduce LDA and its distributed version Parallel LDA (PLDA), along with some popular implementations. Then we propose a systematic solution to evaluate stability and similarity of the trained models and classification results of LDA/PLDA. We address three key challenges within the evaluation solution: (i) topics matching in Kullback Liebler (KL) divergence calculation, (ii) calculation of stability using KL divergence and interpretation of relationship between KL divergence and stability of the trained model and the classification results, (iii) calculation and evaluation of similarity of trained models and classification results. Finally, we experiment with real life datasets to show that our solution is sufficient and efficient.
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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 | 0.000 |
| 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) | 0.000 | 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".