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Detecting And Quantifying Overparametrization In Rna Language Models With Redial.

Home
Detecting And Quantifying Overparametrization In Rna Language Models With Redial.

Related Experiment Video

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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Published on: September 16, 2019

Detecting and quantifying overparametrization in RNA language models with REDIAL.

Da Teng^1,2, Yunrui Qiu^1,2, Gokulakannan Sakthivel²

¹Institute for Health Computing, University of Maryland, Bethesda, Maryland 20852, U.S.A.

Biorxiv : the Preprint Server for Biology

|May 25, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces REDIAL, an unsupervised method to evaluate RNA language models (LMs). REDIAL reveals overparameterization and architectural flaws in current RNA LMs, highlighting inefficiencies in structural prediction.

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning for RNA

Background:

RNA language models (LMs) are crucial foundation models (FMs) for RNA structural prediction.
Current evaluation methods using supervised tasks can obscure FM inefficiencies and memorization.
A need exists for unsupervised frameworks to assess RNA LM performance directly.

Purpose of the Study:

Introduce REDIAL (RNA Embedding perturbation Diagnostics for Language models), a zero-shot, unsupervised framework.
Evaluate how RNA LMs internalize structural constraints by analyzing their latent spaces.
Identify architectural design flaws and parameterization inefficiencies in current RNA LMs.

Main Methods:

Developed REDIAL, an unsupervised framework for extracting coevolutionary signals from RNA LM latent spaces.

Performed layer-wise dissection and ablation studies on popular RNA LMs.

Compared structure-guided pretraining with sequence-only baselines.

Main Results:

Uncovered significant layer-wise disparities in how RNA LMs learn structural constraints.
Demonstrated that RNA LMs are often overparameterized relative to available RNA sequence diversity, leading to inefficiency and overfitting.
Showed that structure-guided pretraining enhances the signal-to-noise ratio of coevolutionary couplings compared to sequence-only methods.

Conclusions:

REDIAL exposes critical flaws in current RNA LM parameter scaling strategies.
Current RNA LM architectures exhibit design flaws leading to parameter inefficiency.
This unsupervised benchmark guides the development of more efficient and generalizable RNA FMs for therapeutics and de novo design.