International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Enhancing LLMs with Indian Multi-Lingual Audio Understanding for AGI Advancement: A Survey Aniruddha Birage1, Tanay Thatte1, Chiranjeev Patil1, Aarush Balkundi1, Arati Deshpande1 Saswati Rabha2, Chintan Parikh2 1PICT Pune (of Aff. SPPU), Pune, India 2SReverie Language Technologies Limited, Bengaluru, India
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Abstract - The recent trends in speech and language
code-switching and zero-shot learning with such successful applications of the model in languages that it was never explicitly trained on.
processing include self-supervised learning, multilingual automatic speech recognition, and large-scale language models. New emerging techniques, such as Wav2Vec 2.0 and joint supervised-unsupervised training, can achieve scalability with high performance in low-resource languages, but challenges such as high computational costs may be incurred, and data imbalances will be encountered. Among the newer innovations with few-shot learning, multimodal models, and task generalization, big improvements come on adaptation and efficiency-while similarly so do the challenges of bias and even resource intensity. This paper will explore these breakthroughs and their effects in discipline.
The multilingual audio addition from India enhances significantly the performance and the adaptability of the LLMs; bidirectional circuits leverage India's linguistic diversity to provide richer, more nuanced representations, enhancing the processing and understanding of various languages. The challenges with LRLs are addressed, and there is a push forward with cross-lingual transfer learning. Use of multi-lingual audio data enables robust training that captures nuances of various dialects and contexts, culminating in richer, more inclusive, and notably accurate language models.
Key Words: Self-supervised learning, Wav2Vec 2.0, Large scale language models, Multimodal models, Task generalization, Low-resource languages, Speech and language processing
Scaling large language models to sizes that are like Pathways Language Model (PaLM) and Generative Pre-trained Transformer 3 (GPT-3) has completely revolutionized the nature of natural language processing. These models excel at learning from few examples and adapting to various tasks with minimal task-specific fine-tuning.. These kinds of scale come with enormous computational costs and increase memory requirements and larger environmental impacts. Data imbalance, particularly the lack of low-resource languages in training datasets, is a major issue.
1.INTRODUCTION This Recent advances in self-supervised learning (SSL), multilingual automatic speech recognition (ASR), and largescale language models (LLMs) have dramatically impacted the speech and language processing community with so many breakthroughs and establishment of new state-of-theart benchmarks. This paper discusses over three broad challenges: speaker and cross-lingual representation learning (XLRL) and efficient scaling of LLMs. Another crucial factor is minimizing dependence on labelled data, especially for low-resource languages (LRLs). Therefore, self-supervised models like Wav2Vec 2.0 push the boundaries of scalability and accuracy; they do indeed help us find answers to many challenges that appear to arise from limited labelled datasets but concurrently expose new complexities because of computational costs of fine-tuning very complex models for specific tasks or languages.
The rapid growth of large model training raises significant concerns for researchers and organizations with limited resources. Therefore, the open-source models like Vicuna have popped up to democratize artificial general intelligence (AGI) so that the organizations can now deploy it for research and development purposes. Other challenges like imbalanced datasets and the search for more efficient and more inclusive models remain.
2. RESEARCH 2.1 Self-Supervised Recognition
XLRL research has advanced significantly, and models can now learn common patterns across LRLs. The property combined with transfer learning techniques makes the flexibility and inclusiveness of speech recognition systems significant. This capability makes these systems generalize effectively across a wide range of languages, thereby making them highly adaptable. Such flexibility has great promise for
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