History and Evolution of Language Models

The Early Days (2013-2017)

• Word2Vec (2013): Showed that words could be represented as meaningful vectors
• Seq2Seq + Attention (2014-2015): Revolutionized machine translation
• ELMo (2018): Context-dependent word embeddings

The Transformer Revolution (2017)

The paper "Attention Is All You Need" by Vaswani et al. introduced the Transformer architecture, eliminating recurrence in favor of self-attention. This was the breakthrough that made modern LLMs possible.

The GPT Era (2018-2023)

• BERT (2018): Bidirectional pre-training showed massive improvements on NLP benchmarks
• GPT-2 (2019): Demonstrated that scaling up language models leads to surprisingly coherent text generation
• GPT-3 (2020): 175B parameters; introduced in-context learning and few-shot prompting
• ChatGPT (2022): RLHF-trained conversational model that brought LLMs to mainstream awareness
• GPT-4 (2023): Multimodal capabilities, significantly improved reasoning

The Current Landscape (2024-2025)

• Claude (Anthropic): Focus on safety, helpfulness, and honesty. Claude Opus 4 represents state-of-the-art capabilities.
• Open-source models: Llama 3, Mistral, Qwen — making LLMs accessible to everyone
• Specialized models: Code-specific (Codex, CodeLlama), multimodal, and domain-specific LLMs
• Efficiency: Smaller, more capable models through better training data and techniques

🌼 Daisy+ in Action: Why Claude?

Daisy+ chose to build on Claude (Anthropic) rather than GPT — prioritizing safety, long context windows, and strong instruction-following. The platform's AI agents use the latest Claude models to handle everything from customer support to document analysis. Claude's emphasis on being helpful, harmless, and honest aligns perfectly with a business platform where accuracy and trust are non-negotiable.