Language Pair
Language pairs, representing the source and target languages in machine translation, are a central focus in natural language processing research, aiming to improve the accuracy and efficiency of cross-lingual communication. Current research emphasizes developing robust models, such as transformer-based architectures and retrieval-augmented methods, that address challenges like low-resource scenarios, data bias, and the need for diverse and high-quality translations. These advancements have significant implications for multilingual applications, including improved cross-lingual information access, enhanced accessibility of digital content, and the development of more inclusive and equitable technologies.
Papers
Code-Mixer Ya Nahi: Novel Approaches to Measuring Multilingual LLMs' Code-Mixing Capabilities
Ayushman Gupta, Akhil Bhogal, Kripabandhu Ghosh
Cultural Fidelity in Large-Language Models: An Evaluation of Online Language Resources as a Driver of Model Performance in Value Representation
Sharif Kazemi, Gloria Gerhardt, Jonty Katz, Caroline Ida Kuria, Estelle Pan, Umang Prabhakar