validation for llms: An interdisciplinary perspective

The advent of modern neural networks carries the promise of transforming industries worldwide. Yet, the “black box” nature of large language models (LLMs) introduces substantial risk — particularly in high-stakes domains such as life sciences and pharmaceuticals.

Effective validation requires more than code reviews or benchmark scores. It demands a risk-based, interdisciplinary approach that integrates expertise in both data science and the domain being modeled. A biologist, for instance, can spot when a generative model produces biologically implausible hypotheses that might escape a purely technical evaluator.

True validation extends beyond technical metrics. It involves translating complex architectures and training data assumptions into a transparent, testable framework — one that aligns with scientific rigor and regulatory expectations.

As AI systems increasingly shape discovery pipelines, interdisciplinary validation will become the foundation of trust. Building teams that bridge computational and domain knowledge isn’t optional; it’s the key to ensuring LLMs advance science responsibly, rather than simply accelerating it.