Buch, Englisch, 228 Seiten, Format (B × H): 155 mm x 235 mm
First International Workshop, LM4UC@AAAI 2026, Held in Conjunction with AAAI 2026, Singapore, January 27, 2026, Proceedings
Buch, Englisch, 228 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Communications in Computer and Information Science
ISBN: 978-981-9220-55-7
Verlag: Springer Verlag, Singapore
This book constitutes the refereed proceedings of the First International Workshop on Language Models for Underserved Communities, LM4UC@AAAI 2026, held in Singapore, on January 27, 2026.
The 14 full papers and 5 short papers included in this volume were carefully reviewed and selected from 43 submissions. They are organized in topical sections as follows: Evaluation and Benchmarks; and Low-Resource Adaptation.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
.- Evaluation and Benchmarks
.- Curiosity-Driven LLM-as-a-judge for Personalized Creative Judgment.
.- Language Model Morphology Evaluation on Canadian Indigenous Languages.
.- Advancing NLP Equity: A Secondary Benchmark Evaluation of Multilingual Language Models for Underrepresented Languages.
.- Why It Failed: A Benchmark to Evaluate Interpretability.
.- Multilingual Evaluation of Human vs. AI Text Classification with Zero-Shot Analysis of Contemporary LLM Architectures.
.- Joint Evaluation: A Human + LLM + Multi-Agents Collaborative Framework for Comprehensive AI Safety (Jo.E).
.- Low-Resource Adaptation
.- From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation.
.- Inverse Language Modeling Towards Robust and Grounded LLMs.
.- One Model, Many Worlds: Cross-Lingual Fine-Tuning Can Improve Low-Resource Capabilities of Language Models.
.- Reflective Translation: Enhancing Low-Resource Machine Translation through Self-Reflection.
.- From Bias to Balance: How Multilingual Dataset Composition Affects Tokenizer Performance Across Languages.
.- Safe at the Margins: A General Approach to Safety Alignment in Low-Resource English Languages – A Singlish Case Study.
.- Pluralistic AI Alignment: A Cross-Cultural Pilot Survey.
.- CAMA: A Culturally Adaptive Multi-Agent Framework for Postpartum Depression Support in Multilingual and Low-Resource Settings.
