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Jun 19, 2024
Prompt Pipelines
LLM-based applications can take the form of autonomous agents, prompt chaining or prompt pipelines. These approaches make use of one or more underlying prompt template.
Cobus Greyling
Cobus Greyling
Jun 16, 2024
Active Prompting with Chain-of-Thought for Large Language Models
Active Prompting with Chain-of-Thought for Large Language Models By using AI accelerated human annotation this framework removes uncertainty and introduces reliability via a Chain-Of-Thought baseline.
Cobus Greyling
Cobus Greyling
Jun 14, 2024
Teaching LLMs To Say, “I don’t know”
Teaching LLMs To Say, “I don’t know” Instead of stating that it does not know, LLMs hallucinate. Hallucination can best be described as highly plausible, succinct & reasonable responses from a LLM, but which is factually incorrect.
Cobus Greyling
Cobus Greyling
Jun 12, 2024
Improving Text Embeddings with LLM Generated Synthetic Data
Improving Text Embeddings with LLM Generated Synthetic Data This study examines how the requirement for weak supervision and human labeled data can be replaced with synthetic data for embeddings, at scale.
Cobus Greyling
Cobus Greyling
Jun 10, 2024
LLM Performance Over Time & LLM Task Contamination
LLM Performance Over Time & Task Contamination
Cobus Greyling
Cobus Greyling
Jun 6, 2024
Data Design For Fine-Tuning LLM Long Context Windows
Data Design For Fine-Tuning LLM Long Context Windows Fine-Tune your LLM To fully utilise the available context window.
Cobus Greyling
Cobus Greyling
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