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History·January 2025·8 min read

Large Language Models

LLMNatural Language ProcessingGPTAI Language

TL;DR

Large Language Models process and generate human-like text by predicting the next word based on patterns learned from vast datasets, but they lack true understanding and can produce convincing falsehoods.

KEY LEARNINGS

✓Large Language Models predict likely next words based on patterns, not genuine understanding.
✓Hallucinations are inherent to how LLMs work, not bugs that can be fully eliminated.
✓Sycophancy causes LLMs to prioritize user agreement over accuracy.
✓Retrieval-Augmented Generation can reduce hallucinations by grounding responses in verified sources.
✓Risk-based governance should match controls to the stakes of each deployment use case.

Additional Resources

📄Anthropic: Constitutional AI
Research on training safer language models.
🔧Hugging Face Model Hub
Repository of open-source language models.
📰Google: Attention Is All You Need
The transformer paper that enabled modern LLMs.

Sources & References

Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS.
Brown, T., et al. (2020). Language Models are Few-Shot Learners. NeurIPS.
Bai, Y., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. Anthropic.

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