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From BoW to GPT: A Brief History of Language Models and Where They’re Going
- Language models have evolved from simple word counters to billion-parameter models, built on decades of breakthroughs.
- The progress from Bag-of-Words (BoW) to n-gram models improved context prediction but lacked understanding.
- Word2Vec introduced word embeddings, representing words as vectors in a relational space.
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks improved language sequence processing.
- The introduction of Transformers in 2017 revolutionized language modeling with self-attention.
- Models like BERT, GPT-2, and T5 are built on the Transformer architecture, focusing on context and relationships.
- Recent advancements have led to massive models like GPT-3 with 175 billion parameters and instruction-tuned models for various tasks.
- Future directions include domain-specific models, neuro-symbolic systems, and connecting language models with real-world data.
- The evolution of language models reflects our evolving understanding of language, culture, and the nuances of communication.
- The future of language models may prioritize smarter, more understanding models over simply scaling up in size.
- Understanding the history of language models is essential in grasping how each step contributes to the evolving capabilities of machines in language processing.
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