LLMs such as GPTs are considered a breakthrough event as they represent a significant leap in how machines understand and generate human language. We can appreciate their transformative influence by considering following points.
1. Multi-tasking : LLMS can perform a wide variety of tasks: coding, translation, question-answers, tutoring and summarization without task-specific programming. Previous models required rules for each task or there were separate models for different tasks. LLMs broke this barrier.
2. Understanding and Generation of Language: These models produce text that is coherent, context-aware and that could not be distinguished from human writing. They can grasp nuances, tone and even reasoning. They are highly fluent.
3. Non-linear scaling: As models become larger, say with billions to trillions of parameters, their abilities improve non-linearly. It is the scaling effect.
4. Few-shot and Zero-shot Learning: Without explicit training, LLMs can complete certain tasks (few shot) or none at all (Zero shot). It becomes possible because of emergent generalization, rare in previous ML systems.
5. Fine tuning: A model need not be trained for all possible tasks. A foundation model can be fine-tuned by separately training for specific tasks, say healthcare.
6. Democratization and Productivity: AI systems make several skills available to non-technical users. They boost productivity of manpower.
7. Beyond Language: LLMs are basically designed for text, but also display reasoning, planning and some symbolic logic. It hints towards a journey to AGI.
8. Innovation: LLMs accelerate R&D in the fields of drug discovery, protein identification and software engineering.
LLMs are breakthroughs by a combination of competence and generalization capability. They emerged suddenly with massive capability jumps. They have widespread influence across several industries. They suggest the beginning of more advanced ML.
In short, the timeline is shown below.
Pre-LLM Era (Before 2017)
Word2vec (2013, Seq2Seq (2014) and Attention Mechanism (2015)
Transformer Era
Transformer model (2017)
Early LLMs and Unsupervised Pretraining
BERT (2018), GPT-2 (2019)
Scaling Breakthrough
GPT-3 (2020)
Mainstream LLMs
ChatGPT (2022)
GitHub Co-pilot (2022)
BLOOM (2022)
Multi-modal and Agentic Intelligence
GPT-4 (2023)
Claude (2023)
Auto-GPT(2023)
LLMs as Platforms
GPT-4 Turbo (2024)
Gemini 1.5 (2024)
Sora (2024-25)
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