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cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…