4 papers
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.…
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…
Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models
Qiawen Ella Liu, Marina Dubova, Henry Conklin +2
Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing. Are large language models (LLMs) creative in the same way humans ar…
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation
Henry Conklin
Despite the remarkable success of large large-scale neural networks, we still lack unified notation for thinking about and describing their representational spaces. We lack methods…