6 papers
Evolving LLM-Generated Features for Interpretable Classification
Jack Butler, Zainab Afolabi, Nikita Kozodoi
Large language models (LLMs) are increasingly used as classifiers, yet they operate as opaque systems whose decisions are difficult to interpret, which complicates their use in reg…
Test-Time Augmentation for LLMs: Where to Inject Diversity at Inference Time
Nikita Kozodoi, Zainab Afolabi, Jack Butler
Aggregating several LLM responses to the same question can improve accuracy, but every method that does so must decide where the responses should differ. Established test-time scal…
Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs
Nikita Kozodoi, Zainab Afolabi, Jack Butler
Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges experts at their optimal…
SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code
Natalia Tarasova, Enrique Balp-Straffon, Aleksei Iancheruk +10
Building infrastructure-as-code (IaC) in cloud computing is a critical task, underpinning the reliability, scalability, and security of modern software systems. Despite the remarka…
Finding the Sweet Spot: Trading Quality, Cost, and Speed During Inference-Time LLM Reflection
Jack Butler, Nikita Kozodoi, Zainab Afolabi +2
As Large Language Models (LLMs) continue to evolve, practitioners face increasing options for enhancing inference-time performance without model retraining, including budget tuning…
ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models
Adrian Mirza, Nawaf Alampara, Martiño Ríos-García +12
Foundation models have shown remarkable success across scientific domains, yet their impact in chemistry remains limited due to the absence of diverse, large-scale, high-quality da…