Look Beyond The Horizon
Dipta Chakraborty
Machine Learning|Deep Learning|Data Science|Generative AI|Azure Certified (4x)|Indian Statistical Institute
Introduction: Evolution of NLP
The groundbreaking moment in the field of Natural Language Processing (NLP) occurred with the introduction of the attention mechanism. In 2017, the Transformer architecture brought a revolutionary shift by incorporating self-attention, allowing models to efficiently capture word dependencies. Prior to this breakthrough, NLP struggled to grasp contextual nuances. In 2018, Google unveiled BERT, and in 2019, OpenAI introduced GPT, both of which showcased the enormous potential of NLP applications. These models predominantly rely on the attention mechanism and the Transformer architecture. They are referred to as "Large Language Models" because they have been trained with a vast number of parameters, enabling them to perform various tasks, including text comprehension, language translation, text generation, and more.
Aura of LLMs
Following the transformative introduction of Transformers, along with the subsequent development of BERT and GPT, there has been a notable shift in approaching NLP-related applications. Many companies and institutions have embarked on the endeavor to create large language models, exploring various architectural designs, including encoder-only, encoder-decoder, and decoder-only models. Since 2019, healthy competition has emerged among these entities, all striving to produce LLMs that can address a multitude of use cases and surpass their predecessors in terms of performance and parameter optimization.
Over the past four to five years, several institutions have introduced numerous LLMs, including:
Finalizing LLM for Use case:
With a plethora of Large Language Models (LLMs) at their disposal, users often face the challenging task of pinpointing the most suitable LLM for their specific use case. Within the realm of NLP, diverse use cases exist, including text classification, text summarization, logical reasoning, code generation, and more. In the process of selecting the ideal LLM for a particular use case, users must consider various critical factors, including:
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Even after navigating through these challenges, users may find themselves left with a shortlist of 2-3 potential models for each use case, making the final selection a formidable task. Often, users opt to implement their use case with all available models and then assess and evaluate their performance before arriving at a definitive choice. This pragmatic approach helps ensure that the chosen LLM aligns optimally with the specific needs and demands of the task at hand.
Conclusion: Look beyond the horizon: Create one platform for LLMs
Tech giants are engaged in a rapid race to pioneer improved Large Language Models (LLMs), striving to outdo their competitors. While this competition fosters the development of more advanced LLMs, it also gives rise to various challenges:
Given these challenges and to simplify the lives of AI developers and data scientists, it is time for major institutions and tech giants to unite under a single platform for collaborative research and LLM development. To enhance efficiency and push the boundaries of AI, these entities should extend their collective capabilities, integrating the expertise of diverse minds and organizations. The overarching objective should be to propel AI into the future, beginning with the journey of advancing Large Language Models, looking beyond current horizons for innovative solutions. Look beyond the horizon!!!