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The limits of bio-molecular modeling with large language models : a cross-scale evaluation

arXiv:2604.03361v1 Announce Type: new Abstract: The modeling of bio-molecular system across molecular scales remains a central challenge in scientific research. Large language models (LLMs) are increasingly applied to bio-molecular discovery, yet systematic evaluation across multi-scale biological problems and rigorous assessment of their tool-augmented capabilities remain limited. We reveal a systematic gap between LLM performance and mechanistic understanding through the proposed cross-scale bio-molecular benchmark: BioMol-LLM-Bench, a unified framework comprising 26 downstream tasks that covers 4 distinct difficulty levels, and computational tools are integrated for a more comprehensive evaluation. Evaluation on 13 representative models reveals 4 main findings: chain-of-thought data provides limited benefit and may even reduce performance on biological tasks; hybrid mamba-attention architectures are more effective for long bio-molecular sequences; supervised fine-tuning improves sp

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Yaxin Xu, Yue Zhou, Tianyu Zhao, Fengwei An, Zhixiang Ren · · 1 min read · 15 views
#cs.LG #q-bio.QM

arXiv:2604.03361v1 Announce Type: new Abstract: The modeling of bio-molecular system across molecular scales remains a central challenge in scientific research. Large language models (LLMs) are increasingly applied to bio-molecular discovery, yet systematic evaluation across multi-scale biological problems and rigorous assessment of their tool-augmented capabilities remain limited. We reveal a systematic gap between LLM performance and mechanistic understanding through the proposed cross-scale bio-molecular benchmark: BioMol-LLM-Bench, a unified framework comprising 26 downstream tasks that covers 4 distinct difficulty levels, and computational tools are integrated for a more comprehensive evaluation. Evaluation on 13 representative models reveals 4 main findings: chain-of-thought data provides limited benefit and may even reduce performance on biological tasks; hybrid mamba-attention architectures are more effective for long bio-molecular sequences; supervised fine-tuning improves specialization at the cost of generalization; and current LLMs perform well on classification tasks but remain weak on challenging regression tasks. Together, these findings provide practical guidance for future LLM-based modeling of molecular systems.

Executive Summary

This article presents a cross-scale evaluation of large language models (LLMs) for bio-molecular modeling, revealing a systematic gap between LLM performance and mechanistic understanding. The authors propose the BioMol-LLM-Bench, a unified framework for evaluating LLMs on 26 downstream tasks across 4 difficulty levels. The results highlight the limitations of current LLMs, including their weakness on regression tasks and the trade-off between specialization and generalization. The study provides practical guidance for future LLM-based modeling of molecular systems, emphasizing the need for more robust and specialized models. The findings have significant implications for the field of bio-molecular modeling, highlighting the need for more nuanced understanding of LLM capabilities and limitations.

Key Points

  • ▸ The BioMol-LLM-Bench framework provides a comprehensive evaluation of LLMs on bio-molecular tasks.
  • ▸ Current LLMs perform well on classification tasks but struggle with regression tasks.
  • ▸ Hybrid mamba-attention architectures and supervised fine-tuning improve LLM performance on bio-molecular tasks.

Merits

Systematic evaluation framework

The BioMol-LLM-Bench framework provides a comprehensive and systematic evaluation of LLMs on bio-molecular tasks, filling a significant gap in the field.

Practical guidance

The study provides practical guidance for future LLM-based modeling of molecular systems, emphasizing the need for more robust and specialized models.

Demerits

Methodological limitations

The study relies on a limited number of models and tasks, which may not be representative of the broader LLM landscape.

Lack of mechanistic understanding

The study highlights a systematic gap between LLM performance and mechanistic understanding, which may require further investigation to address.

Expert Commentary

The article presents a comprehensive evaluation of LLMs for bio-molecular modeling, highlighting the limitations of current models and providing practical guidance for future development. However, the study relies on a limited number of models and tasks, which may not be representative of the broader LLM landscape. Furthermore, the study highlights a systematic gap between LLM performance and mechanistic understanding, which may require further investigation to address. Nevertheless, the study provides significant insights into the limitations of current LLMs and highlights the need for more robust and specialized models, capable of handling complex bio-molecular tasks.

Recommendations

  • ✓ Future research should prioritize the development of more robust and specialized LLMs, capable of handling complex bio-molecular tasks.
  • ✓ The BioMol-LLM-Bench framework should be expanded to include more models, tasks, and difficulty levels to provide a more comprehensive evaluation of LLMs.

Sources

Original: arXiv - cs.LG

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