Large Language Models
- Large Language Model
- LLM
- Large Language Models
GLHF uses unquantified models, so they're more adjusted
Big players
American
European
Asian
- deepseek
- kimi
Connections
- Related Concepts: AI Agents (applications powered by LLMs), Prompt Engineering (interface method), Token Economics (resource constraints)
- Broader Context: Deep Learning (parent field), Natural Language Processing (application domain)
- Applications: RAG Systems (knowledge enhancement), Fine-tuning (customization approach)
- Components: Transformer Architecture (technical foundation), RLHF (alignment method)
- Related Concepts:
- Transformer Neural Networks: The architecture underlying modern LLMs.
- Natural Language Processing: The broader field LLMs have revolutionized.
- Chatbots: Applications that frequently leverage LLMs.
- Broader Concepts:
- Deep Learning: The machine learning approach used in LLMs.
- AI Ethics: Concerns about LLM biases, hallucinations, and social impacts.
Resources
References
- Attention Is All You Need (Vaswani et al., 2017)
- Language Models are Few-Shot Learners (Brown et al., 2020)
- Training Language Models to Follow Instructions with Human Feedback (Ouyang et al., 2022)
- Scaling Laws for Neural Language Models (Kaplan et al., 2020)
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- Primary Source:
- "Language Model History: Before and After Transformer" (Medium, cited in TechTarget article)
- Additional Resources:
- TechTarget's overview of LLMs in chatbot applications
- Research papers on transformer architecture and scaling laws