Does Combining Parameter‐Efficient Modules Improve Few‐Shot Transfer Accuracy?
Bibliographic record
Abstract
ABSTRACT Parameter‐efficient fine‐tuning has become a key technique for adapting large language and vision models to diverse downstream tasks, including emerging applications in engineering, such as design optimisation, fault detection in complex systems, material property prediction and autonomous robotics control. Specifically, the efficiency of low‐rank adaptation has facilitated the creation and sharing of hundreds of custom low‐rank adaptation (LoRA) modules, each trained on distinct data from various downstream tasks. In this paper, we explore the composability of LoRA modules, examining if combining these pre‐trained modules enhances generalisation to unseen downstream tasks. Our investigation involves evaluating two approaches: (a) uniform composition , involving averaging upstream LoRA modules with equal weights and (b) learned composition , where we learn the weights for each upstream module and perform weighted averaging. Through experiments on both vision and language models, we demonstrate that in few‐shot settings—common in many engineering workflows such as structural design, material discovery and system diagnostics—both composition methods consistently outperform full fine‐tuning and training LoRA from scratch. Moreover, in full‐shot settings, learned composition matches the performance of standard LoRA training while requiring significantly fewer trainable parameters. These results highlight the potential of compositional parameter‐efficient adaptation as a scalable and flexible approach for AI‐driven engineering tasks, enabling faster deployment, reduced data requirements and improved model reusability across diverse domains.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".