3 papers
cs.CL2026
DataProphet: Demystifying Supervision Data Generalization in Multimodal LLMs
Xuan Qi, Luxi He, Dan Roth +1
Conventional wisdom for selecting supervision data for multimodal large language models (MLLMs) is to prioritize datasets that appear similar to the target benchmark, such as text-…
cs.LG2026
On Improving Neurosymbolic Learning by Exploiting the Representation Space
Aaditya Naik, Efthymia Tsamoura, Shibo Jin +2
We study the problem of learning neural classifiers in a neurosymbolic setting where the hidden gold labels of input instances must satisfy a logical formula. Learning in this sett…
cs.LG2025
Imbalances in Neurosymbolic Learning: Characterization and Mitigating Strategies
Kaifu Wang, Efthymia Tsamoura, Dan Roth
We study one of the most popular problems in **neurosymbolic learning** (NSL), that of learning neural classifiers given only the result of applying a symbolic component to th…