Adapting Large Language Models For Indian Languages Adasci

Adapting Large Language Models For Indian Languages - ADaSci

Adapting Large Language Models For Indian Languages - ADaSci Adapting large language models for indian languages marks a pivotal step towards embracing linguistic diversity in ai, unlocking new possibilities for millions of native speakers. Exciting developments at mlds 2024 as we delve into 'adapting large language models for indian languages' by abhinand balchandran. his work on tamil llama an.

INDIAai

INDIAai Our model is specifically fine tuned to the 11 indian languages mentioned above over millions of sentences. the model is then benchmarked over a human annotated testset and multiple other publicly available indian ner datasets. Through extensive experiments fine tuning models on small parallel data, we evaluate the improvements enabled by adapting pretrained models to a particular language or domain, and analyze model capabilities for handling indian language diversity. Covering major languages such as hindi, bengali, gujarati, marathi, kannada, punjabi, tamil, telugu, and urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the indian subcontinent. Looking ahead, we are committed to expanding our pretraining corpora to support the development of even more robust generative models, while ensuring diversity in their generation capabilities, thereby advancing the frontier of language technology for india’s diverse linguistic landscape.

Large Language Models: Does India Need To Build One?

Large Language Models: Does India Need To Build One? Covering major languages such as hindi, bengali, gujarati, marathi, kannada, punjabi, tamil, telugu, and urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the indian subcontinent. Looking ahead, we are committed to expanding our pretraining corpora to support the development of even more robust generative models, while ensuring diversity in their generation capabilities, thereby advancing the frontier of language technology for india’s diverse linguistic landscape. Its improved multilingual understanding and generation capabilities on indian languages makes it the model of choice as a backbone in large multimodal models for visual understanding, captioning, and speech applications in the indian context. Several efforts are currently underway to develop large language models (llms) tailored to indian languages. building these models requires addressing some key challenges, such as creating indian language datasets and training models in a way that they work with nuances of indian languages. Developing large language models (llms) for india’s rich linguistic spectrum faces unique challenges, like diverse languages and dialects, each with its grammar, syntax, cultural context,. This paper, we present a methodology for converting unstructured text into a structured question and answer format, specifically targeting 11 indian languages. the scarcity of question and answer datasets for these languages poses a significant challenge for fine tuning large language models (llms) for specific tasks.

Adapting Large Language Models for Indian Languages | Abhinandan Balchandran | Kaggle Master