Large Language Model (LLM) Strategy
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An Large Language Model (LLM) Strategy is an AI strategy focused on large language model (LLM) technologies.
- Context:
- It can address the development, deployment, and management of LLMs within an organization.
- It can include choosing appropriate LLM architectures, such as transformer-based models.
- It can include determining success metrics for LLM performance and value generation.
- It can include strategies for LLM training, such as pre-training on large corpora and fine-tuning for specific tasks.
- It can include approaches for handling LLM hallucination and improving LLM factual accuracy.
- It can include prompt engineering techniques for optimizing LLM performance.
- It can address AI ethics considerations specific to LLMs, such as mitigating bias in language models.
- It can include tactics for efficient LLM inference, such as model compression techniques.
- It can encompass leveraging LLMs for various natural language processing (NLP) tasks, such as text generation, text summarization, question answering, named entity recognition, etc.
- ...
- Example(s):
- An Open-Source LLM Strategy, leveraging models like GPT-Neo, GPT-J, and BLOOM.
- A Commercial LLM Strategy, utilizing offerings like OpenAI API, Anthropic API, or Cohere API.
- A Hybrid LLM Strategy, combining open-source models for experimentation with commercial offerings for production.
- a Organization-Specific LLM Strategy, such as:
- OpenAI's LLM Strategy, centered around iterating on GPT architectures and InstructGPT training.
- DeepMind's LLM Strategy, focused on scaling laws and constitutional AI.
- Anthropic's LLM Strategy, emphasizing AI safety via constitutional AI techniques like AI constitutional principles.
- ...
- Counter-Example(s):
- A Computer Vision AI Strategy focused on image recognition and object detection.
- A Symbolic AI Strategy utilizing knowledge graphs and expert systems.
- See: AI Strategy, NLP Strategy, Prompt Engineering, LLM Training, LLM Inference