Few shot learning approaches to essay scoring
Bibliographic record
Abstract
Automated essay scoring (AES) involves using computer technology to grade written assessments and assigning a score based on their perceived quality. AES has been among the most significant Natural Language Processing (NLP) applications due to its educational and commercial value. Similar to many other NLP tasks, training a model for AES typically involves acquiring a substantial amount of labeled data specific to the essay being graded. This usually incurs a substantial cost. In this study, we consider two recent few-shot learning methods to enhance the predictive performance of machine learning methods for AES tasks. Specifically, we experiment with a prompt-based few-shot learning method, pattern exploiting training (PET), and a prompt-free few-shot learning strategy, SetFit, and compare these against vanilla fine-tuning. Our numerical study shows that PET can provide substantial performance gains over other methods, and it can effectively boost performance when access to labeled data is limited. On the other hand, PET is found to be the most computationally expensive few-shot learning method considered, whileSetFit is the fastest method among the approaches.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".