Summary of Llms Are Highly-constrained Biophysical Sequence Optimizers, by Angelica Chen et al.
LLMs are Highly-Constrained Biophysical Sequence Optimizers
by Angelica Chen, Samuel D. Stanton, Robert G. Alberstein, Andrew M. Watkins, Richard Bonneau, Vladimir Gligorijević, Kyunghyun Cho, Nathan C. Frey
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential of large language models (LLMs) in biological tasks such as protein engineering and molecule design. LLMs excel at generating discrete sequences, but struggle with fine-grained constraints typical in biology. To address this, the authors propose Language Model Optimization with Margin Expectation (LLOME), a bilevel optimization approach that combines offline and online optimization using limited oracle evaluations. The methodology also incorporates a novel training objective, Margin-Aligned Expectation (MargE), which trains the LLM to smoothly interpolate between reward and reference distributions. A synthetic test suite is introduced to evaluate LLM optimizers rapidly without requiring lab validation. Results show that LLMs outperform genetic algorithm baselines in finding lower-regret solutions with fewer function evaluations, but also exhibit moderate miscalibration and susceptibility to generator collapse when explicit ground truth rewards are unavailable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help us design new proteins and molecules. These models are good at coming up with sequences of letters that make sense biologically, but they have trouble following specific rules we need them to follow. To solve this problem, the researchers created a new way for the models to work called LLOME (Language Model Optimization with Margin Expectation). They also developed a special training technique called MargE (Margin-Aligned Expectation) that helps the models make better decisions. The team built a test suite to see how well the models do and found that they can find good solutions quickly, but sometimes they make mistakes or get stuck. |
Keywords
* Artificial intelligence * Language model * Optimization
