Developing Prefix-Tuning Models for Hierarchical Text Classification
Bibliographic record
Abstract
Hierarchical text classification (HTC) is a key problem and task in many industrial applications, which aims to predict labels organized in a hierarchy for given input text.For example, HTC can group the descriptions of online products into a taxonomy or organizing customer reviews into a hierarchy of categories.In real-life applications, while Pre-trained Language Models (PLMs) have dominated many NLP tasks, they face significant challenges too-the conventional fine-tuning process needs to modify and save models with a huge number of parameters.This is becoming more critical for HTC in both global and local modelling-the latter needs to learn multiple classifiers at different levels/nodes in a hierarchy.The concern will be even more serious since PLM sizes are continuing to increase in order to attain more competitive performances.Most recently, prefix tuning has become a very attractive technology by only tuning and saving a tiny set of parameters.Exploring prefix turning for HTC is hence highly desirable and has timely impact.In this paper, we investigate prefix tuning on HTC in two typical setups: local and global HTC.Our experiment shows that the prefix-tuning model only needs less than 1% of parameters and can achieve performance comparable to regular full fine-tuning.We demonstrate that using contrastive learning in learning prefix vectors can further improve HTC performance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".