E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models
Bibliographic record
Abstract
Building huge and highly capable language models has been a trend in the past years.Despite their great performance, they incur high computational cost.A common solution is to apply model compression or choose light-weight architectures, which often need a separate fixed-size model for each desirable computational budget, and may lose performance in case of heavy compression.This paper proposes an effective dynamic inference approach, called E-LANG, which distributes the inference between large accurate Supermodels and light-weight Swift models.To this end, a decision making module routes the inputs to Super or Swift models based on the energy characteristics of the representations in the latent space.This method is easily adoptable and architecture agnostic.As such, it can be applied to black-box pre-trained models without a need for architectural manipulations, reassembling of modules, or re-training.Unlike existing methods that are only applicable to encoder-only backbones and classification tasks, our method also works for encoderdecoder structures and sequence-to-sequence tasks such as translation.The E-LANG performance is verified through a set of experiments with T5 and BERT backbones on GLUE, Su-perGLUE, and WMT.In particular, we outperform T5-11B with an average computations speed-up of 3.3× on GLUE and 2.9× on SuperGLUE.We also achieve BERT-based SOTA on GLUE with 3.2× less computations.Code and demo are available here.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".