3 papers
cs.CL2025
Continual Learning via Sparse Memory Finetuning
Jessy Lin, Luke Zettlemoyer, Gargi Ghosh +4
Modern language models are powerful, but typically static after deployment. A major obstacle to building models that continually learn over time is catastrophic forgetting, where u…
cs.CL2025
Learning Facts at Scale with Active Reading
Jessy Lin, Vincent-Pierre Berges, Xilun Chen +3
LLMs are known to store vast amounts of knowledge in their parametric memory. However, learning and recalling facts from this memory is known to be unreliable, depending largely on…
cs.IR2024
CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval
Yizhou Chi, Jessy Lin, Kevin Lin +1
Users often make ambiguous requests that require clarification. We study the problem of asking clarification questions in an information retrieval setting, where systems often face…