Attention-Guided Federated Learning for Privacy-Preserving Clinical NLP

Mon, 01 Jun 2026 00:00:00 +0000

Abstract

Training clinical natural language processing models typically requires centralizing patient records, which is often legally or ethically infeasible across institutions. We introduce an attention-guided federated learning protocol that allows clinical language models to be trained collaboratively across multiple hospital systems without any patient data leaving its host institution.

Low-Resource Adaptation of Large Language Models for Regional Dialects

Fri, 01 Nov 2024 00:00:00 +0000

Abstract

Large language models perform unevenly across regional dialects that are underrepresented in web-scale training corpora. We propose a parameter-efficient adaptation method that fine-tunes a small set of dialect-specific adapter layers using as few as 5,000 labeled examples, substantially closing the performance gap on downstream tasks including sentiment analysis, named entity recognition, and machine translation for four regional dialect groups.

NLP on Abhiraj Bibhar

Attention-Guided Federated Learning for Privacy-Preserving Clinical NLP

Abstract

Low-Resource Adaptation of Large Language Models for Regional Dialects

Abstract