LLM s for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024)

Abstract: The recent breakthroughs in Artificial Intelligence (AI) can be attributed to the remarkable performance of Large Language Models (LLMs) across a spectrum of research areas (e.g., machine translation, question-answering, automatic speech recognition, text-to-speech generation) and application domains (e.g., business, law, healthcare, education, and psychology). The success of these LLMs largely de- pends on specific training techniques, most notably instruction tuning, RLHF, and subsequent prompting to achieve the desired output. As the development of such LLMs continues to increase in both closed and open settings, evaluation has become crucial for understanding their generalization capabilities across different tasks, modalities, languages, and dialects. This evaluation process is tightly coupled with prompting, which plays a key role in obtain- ing better outputs. There has been attempts to evaluate such models focusing on diverse tasks, languages, and dialects, which suggests that the capabilities of LLMs are still limited to medium-to-low-resource languages due to the lack of representative datasets. The tutorial offers an overview of this emerging research area. We explore the capabilities of LLMs in terms of their performance, zero- and few-shot settings, fine-tuning, instructions tuning, and close vs. open models with a special emphasis on low-resource settings. In addition to LLMs for standard NLP tasks, we will focus on speech and multimodality.

## tutorial content description large language models (llms) are prominent examples of foundation models (fms), based on the transformer network architecture @xcite . trained to predict the subsequent token in a sequence, llms capture implicit and intricate information contained in the data. moreover, when created using multilingual training data, the models capture linguistic nuances, phonological patterns, and semantic relationships across languages, strengthening its multilingual capabilities. however, understanding how their capabilities generalize across tasks and languages requires a systematic evaluation approach. ## type of the tutorial the tutorial is both introductory, covering a number of topics related to the capabilities of llms, but it is also cutting-edge, covering some latest developments in these areas. attendees will have an overview of tasks, languages, dialects and modalities related to llms, which will put them up to speed to do research in the area. the tutorial targets anyone interested in employing llms for nlp, speech and multimodal tasks. we believe researchers working on lower-resource languages will be especially interested. we expect the audience to have intermediate machine learning knowledge. ## outline of the tutorial below, we offer an outline of the tutorial. more information and materials will is available online on the tutorial website upon the tutorial acceptance. references: @xcite @xcite @xcite @xcite 3.2 models and their capabilities for low-resource languages [30 min] ## prerequisites we expect attendees to be equipped with basic knowledge of machine learning, including familiarity with recent neural network architectures, particularly transformers, and an understanding of pretrained language models. additionally, attendees should be familiar with standard nlp tasks such as text classification, natural language generation, and question answering. ## reading list in addition to the references cited in section 3, we recommend several surveys: an overview of llms @xcite , prompt engineering @xcite , in-context learning @xcite , and evaluation of llms @xcite . firoj alam is a scientist at the qatar computing research institute (qcri), hbku. he received his phd from the university of trento, italy, and has been working for more than ten years in artificial intelligence, deep/machine learning, natural language processing, social media content, image processing, and conversation analysis. his current research interest includes llms, factchecking, multimodal propaganda detection in multiple languages. he previously presented tutorials at www-2022 and wsdm-2022 on the topic of "fact-checking, fake news, propaganda, and media bias". he was a co-organizer of different shared tasks checkthat! 2020-2024 at clef, semeval-2021 task 6 (propaganda detection in memes), semeval-2024 task (multilingual detection of persuasion techniques in memes), wanlp (arabic-nlp) shared task (2022-2023) and the nlp4if-2021 shared task. he is also a co-organizer of the blp-2023 workshop (collocated with emnlp-2023). shammur absar chowdhury is a scientist at qcri, hbku. her research interest includes designing speech models, and interpretability for atypical phenomena in conversation. dr. chowdhury authored more than 60 peer-reviewed publications in tier-top conferences and journals; and actively contributed to the research community by organizing shared tasks, challenges, and workshops like semeval-2022 (task 3), qasr-tts-v1.0 (asru2023), slt2023 (local chair), summer workshop jsalt2022 (as a senior mentor) ## ethics statement our tutorial is based on our own work in the area, related studies and public sources. credit will be given wherever needed. any biases are unintended.