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ARXIV:2603.14593 · QUALITY ESTIMATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.14593QUALITY ESTIMATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Parameter-efficient quality estimation for low-resource languages using frozen recursive models.
Opportunity summary
Pain Parameter-efficient quality estimation for low-resource languages using frozen recursive models.
Evidence 0 refs | 0 sources | 17% coverage
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Parameter-efficient quality estimation for low-resource languages using frozen recursive models. We investigate whether these recursive mechanisms transfer to Quality Estimation (QE) for low-resource languages using a three-phase methodology.
Tiny Recursive Models (TRM) achieve strong results on reasoning tasks through iterative refinement of a shared network. We investigate whether these recursive mechanisms transfer to Quality Estimation (QE) for low-resource languages using a three-phase…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Tiny Recursive Models (TRM) achieve strong results on reasoning tasks through iterative refinement of a shared network.
Quality Estimation moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Parameter-efficient quality estimation for low-resource languages using frozen recursive models.
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10.48550/arXiv.2603.14593Parameter-efficient quality estimation for low-resource languages using frozen recursive models.
Abstract
Tiny Recursive Models (TRM) achieve strong results on reasoning tasks through iterative refinement of a shared network. We investigate whether these recursive mechanisms transfer to Quality Estimation (QE) for low-resource languages using a three-phase methodology. Experiments on $8$ language pairs on a low-resource QE dataset reveal three findings. First, TRM's recursive mechanisms do not transfer to QE. External iteration hurts performance, and internal recursion offers only narrow benefits. Next, representation quality dominates architectural choices, and lastly, frozen pretrained embeddings match fine-tuned performance while reducing trainable parameters by 37$\times$ (7M vs 262M). TRM-QE with frozen XLM-R embeddings achieves a Spearman's correlation of 0.370, matching fine-tuned variants (0.369) and outperforming an equivalent-depth standard transformer (0.336). On Hindi and Tamil, frozen TRM-QE outperforms MonoTransQuest (560M parameters) with 80$\times$ fewer trainable parameters, suggesting that weight sharing combined with frozen embeddings enables parameter efficiency for QE. We release the code publicly for further research. Code is available at https://github.com/surrey-nlp/TRMQE.
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PROBLEM
Parameter-efficient quality estimation for low-resource languages using frozen recursive models. We investigate whether these recursive mechanisms transfer to Quality Estimation (QE) for low-resource languages using a three-phase methodology.
METHOD
Tiny Recursive Models (TRM) achieve strong results on reasoning tasks through iterative refinement of a shared network. We investigate whether these recursive mechanisms transfer to Quality Estimation (QE) for low-resource languages using a three-phase methodology.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Tiny Recursive Models (TRM) achieve strong results on reasoning tasks through iterative refinement of a shared network.
WHY NOW
Quality Estimation moved forward this cycle; last verified April 2026. Public score 8.0/10.
TRM's recursive mechanisms do not transfer to QE. External iteration hurts performance, and internal recursion offers only narrow benefits.
Directly stated in abstract with clear negative finding
partial
frozen pretrained embeddings match fine-tuned performance while reducing trainable parameters by 37× (7M vs 262M).
Explicitly stated with precise numeric comparison in abstract
partial
TRM-QE with frozen XLM-R embeddings achieves a Spearman's correlation of 0.370, matching fine-tuned variants (0.369)
Direct numeric results provided in abstract with specific correlation values
partial
On Hindi and Tamil, frozen TRM-QE outperforms MonoTransQuest (560M parameters) with 80× fewer trainable parameters.
Specific language pairs and parameter reduction explicitly stated
partial
representation quality dominates architectural choices
Directly stated as a key finding in abstract
partial
weight sharing combined with frozen embeddings enables parameter efficiency for QE.
Conclusion directly stated in abstract based on experimental results
partial
Experiments on $8$ language pairs on a low-resource QE dataset
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partial
using a three-phase methodology
Direct description of methodology in abstract
partial
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Parameter-efficient quality estimation for low-resource languages using frozen recursive models.
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