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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