LLMs通过新优化方法可挑战生物序列难题，但专用工具仍更高效。
Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
- 提出LLOME优化框架，结合偏好学习提升语言模型求解能力
- 在部分合成任务上达到甚至超越专用工具LaMBO-2性能
- 揭示LLM存在奖励误校准问题，需显式奖励才表现良好
尽管大语言模型（LLMs）在生物分子优化中展现潜力，但其计算成本高且难以满足精确约束。相比之下，专用求解器如LaMBO-2效率更高、控制更精细，但需更多领域知识。由于实验验证昂贵且合成基准不足，二者对比困难。为此，本文引入Ehrlich函数——一种捕捉生物物理序列优化几何结构的合成测试集。仅靠提示，现成LLMs难以优化Ehrlich函数。为此，我们提出LLOME（基于边际期望的语言模型优化），一种用于在线黑盒优化的双层优化流程。结合新型偏好学习损失后，LLOME不仅能学会解决部分Ehrlich函数，甚至在中等难度变体上表现与或优于LaMBO-2。然而，LLMs也表现出概率-奖励误校准现象，缺乏显式奖励时表现不佳。结果表明，LLMs偶尔能带来显著优势，但专用求解器仍具竞争力且开销更低。
原文摘要 · Abstract (English)
Although large language models (LLMs) have shown promise in biomolecule optimization problems, they incur heavy computational costs and struggle to satisfy precise constraints. On the other hand, specialized solvers like LaMBO-2 offer efficiency and fine-grained control but require more domain expertise. Comparing these approaches is challenging due to expensive laboratory validation and inadequate synthetic benchmarks. We address this by introducing Ehrlich functions, a synthetic test suite that captures the geometric structure of biophysical sequence optimization problems. With prompting alone, off-the-shelf LLMs struggle to optimize Ehrlich functions. In response, we propose LLOME (Language Model Optimization with Margin Expectation), a bilevel optimization routine for online black-box optimization. When combined with a novel preference learning loss, we find LLOME can not only learn to solve some Ehrlich functions, but can even perform as well as or better than LaMBO-2 on moderately difficult Ehrlich variants. However, LLMs also exhibit some likelihood-reward miscalibration and struggle without explicit rewards. Our results indicate LLMs can occasionally provide significant benefits, but specialized solvers are still competitive and incur less overhead.
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