5 papers
Forget to Know, Remember to Use: Context-Aware Unlearning for Large Language Models
Yuefeng Peng, Parnian Afshar, Megan Ganji +4
Large language models may encode sensitive information or outdated knowledge that needs to be removed, to ensure responsible and compliant model responses. Unlearning has emerged a…
DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English
Jio Oh, Paul Vicinanza, Thomas Butler +3
More than 80% of the 1.6B English speakers do not use Standard American English (SAE), yet LLMs often fail to correctly identify non-SAE dialects and generate stereotyped responses…
English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin +2
Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities acros…
LinguaMap: Which Layers of LLMs Speak Your Language and How to Tune Them?
J. Ben Tamo, Daniel Carlander-Reuterfelt, Jonathan Rubin +3
Despite multilingual pretraining, large language models often struggle with non-English tasks, particularly in language control, the ability to respond in the intended language. We…
PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detection
Ali Lotfi Rezaabad, Bikram Khanal, Shashwat Chaurasia +5
Language identification is a crucial first step in multilingual systems such as chatbots and virtual assistants, enabling linguistically and culturally accurate user experiences. E…