Data-Centric AI for Large Language Foundation Models

Underline Science Inc. 2026 Open access

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Authors: Association for Artificial Intelligence 2026, Bryan Kian Hsiang Low
DOI: 10.48448/vee2-k265
Cited by: 0

Abstract

In this talk, I will discuss the motivation and practical implications of two recent research efforts of my GLOW.AI research group, namely, (1) resource- efficient AI (specifically, how to learn with less data, and (2) AI to check on AI (specifically, data attribution and provenance, and evaluation methods for data removal and machine unlearning).

Fields of Study & Subjects

Privacy-Preserving Technologies in Data Topic Modeling Ethics and Social Impacts of AI

Publication details

Journal / source
Underline Science Inc.
Publication date
20 January 2026
Language
EN