Same model, better performance: the impact of shuffling on DNA Language Models benchmarking

• URL: http://arxiv.org/abs/2510.12617v1
• Date: Tue, 14 Oct 2025 15:16:56 GMT
• Title: Same model, better performance: the impact of shuffling on DNA Language Models benchmarking
• Authors: Davide Greco, Konrad Rawlik,
• Abstract summary: Large Language Models are increasingly popular in genomics due to their potential to decode complex biological sequences.<n>We show that evaluating DNA LMs is a complex task that intersects genomic's domain-specific challenges and machine learning methodologies.<n>We propose a simple solution: pre-shuffling data before storage eliminates hardware dependencies while maintaining efficiency.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models are increasingly popular in genomics due to their potential to decode complex biological sequences. Hence, researchers require a standardized benchmark to evaluate DNA Language Models (DNA LMs) capabilities. However, evaluating DNA LMs is a complex task that intersects genomic's domain-specific challenges and machine learning methodologies, where seemingly minor implementation details can significantly compromise benchmark validity. We demonstrate this through BEND (Benchmarking DNA Language Models), where hardware-dependent hyperparameters -- number of data loading workers and buffer sizes -- create spurious performance variations of up to 4% for identical models. The problem stems from inadequate data shuffling interacting with domain specific data characteristics. Experiments with three DNA language models (HyenaDNA, DNABERT-2, ResNet-LM) show these artifacts affect both absolute performance and relative model rankings. We propose a simple solution: pre-shuffling data before storage eliminates hardware dependencies while maintaining efficiency. This work highlights how standard ML practices can interact unexpectedly with domain-specific data characteristics, with broader implications for benchmark design in specialized domains.

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