Flexible Distribution Approaches to Enhance Regression and Deep Topic Modelling Techniques
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This paper presents an extension of the Dirichlet multinomial regression (DMR) and deep Dirichlet multinomial regression (dDMR) topic modelling approaches by incorporating the generalised Dirichlet (GD) and Beta‐Liouville (BL) distributions using collapsed Gibbs sampling for parameter inference. The DMR and dDMR approaches have been shown to be effective in discovering latent topics in text corpora. However, these approaches have limitations when it comes to handling complex data structures and overfitting issues. To address these limitations, we introduce the GD and BL distributions, which have more flexibility in modelling complex data structures and handling sparse data. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, which provides a computationally efficient method for inference. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach in improving topic modelling performance, particularly in handling complex data structures and reducing overfitting. The proposed models also exhibit good interpretability of the learned topics, making them suitable for various applications in natural language processing and machine learning.
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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) | 0.000 | 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 it