The development of deep learning approaches for modeling these multifactorial effects of GVs is still in its nascent stages, primarily due to the lack of comprehensive datasets that capture the intricate relationships between GVs and their downstream effects on complex traits.

Longer contexts present a new attack surface for adversarial attacks. In search of a "fruit-fly" of long-context vulnerabilities, we study Many-shot Jailbreaking (MSJ; Figure 1), a simple yet effective and scalable jailbreak.

Generative protein language models are a natural way to design new proteins with desired functions.

To bridge this gap, we introduce the BABILong benchmark, designed to test language models' ability to reason across facts distributed in extremely

LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.