Fine-Tuning Optimization of Small Language Models: A Novel Graph-Theoretical Approach for Efficient Prompt Engineering
Why this work is in the frame
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Bibliographic record
Abstract
In the realm of fine-tuning pre-trained language models in modern prompt engineering, we introduce a novel graph-theoretical approach to address the resource-intensive challenges to the conventional data fine-tuning methods for prompt engineering. Leveraging semantic and contextual prompt relationships, we propose to form a novel prompt graph, which facilitates a new comprehensive representation of prompt similarities. Building upon this new graph structure, our proposed approach can minimize the training time during the fine-tuning process for small language models by identifying and utilizing cliques corresponding to condensed subsets of highly similar prompts. This new strategic reduction in training data can greatly reduce the training time, particularly for resource-constrained applications in practice. Our proposed new approach leads to a significant reduction in the original prompt-graph order and a more focused and streamlined fine-tuning process. This data-reduction strategy demonstrates the potential to enable finetuning language models for prompt engineering with smaller datasets subject to less computational resource. The real run-time analysis for the training process of a small language model GPT2 have been undertaken to show the advantage of our proposed new approach.
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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