Large Language Models
Data used to train LLMs
- LLMs are trained in an unsupervised manner on open source, licensed and borrowed data
- Responses are refined using Q/A pairs
- Reinforcement learning with human feedback is used to reward LLMs to give appropriate responses
LLMs can (seem to) be creative
- Context-based learning + randomness allow the LLMs to generate surprising outputs
- Can use LLMs to:
- Identify weakly similar concepts from different disciplines and help understand different disciplines
- Generate narratives etc
Challenges in LLMs
- LLMs may seem to "lie" and "hallucinate"
- This is some function of data, learning, search and probability
- Remember that the output of a LLM is determined by both what the system has been trained on and what information you give it
- Prompt engineering means tailoring your questions and input so you can get the most out of an LLM
Prompting Techniques
- Prompting techniques are about formatting your input to provide a context that the LLM ca use more effectively, restricting the probability distribution of the words it chooses to sample next
Training phases of LLM
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