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cs.AI2026
Similarity All The Way Up: Multilingual Generalization in LLMs Relies on Language-Level Similarity Structures
Supantho Rakshit, Adele Goldberg, Henry Conklin
As Large Language Models (LLMs) grow more capable across diverse tasks, their (in)ability to generalize remains difficult to quantify and poorly understood beyond limited domains.…
cs.LG2026
Learning is Forgetting: LLM Training As Lossy Compression
Henry C. Conklin, Tom Hosking, Tan Yi-Chern +5
Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are structured. This limits our abilit…
cs.AI2026
Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models
Qiawen Ella Liu, Marina Dubova, Henry Conklin +2
Creative ideas often arise by associating remote concepts. Can random associations reliably increase originality, and do they help humans and large language models (LLMs) in the sa…