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Found 5 papers, 2 papers with code
CoLLEGe: Concept Embedding Generation for Large Language Models
Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly.
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa.
Improving Systematic Generalization Through Modularity and Augmentation
After training on an augmented dataset with almost forty times more adverbs than the original problem, a non-modular baseline is not able to systematically generalize to a novel combination of a known verb and adverb.
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same color") and ad-hoc categories defined through goals ("objects that could fall on one's head").
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks
Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills.
