LLM token prediction: How models work and why they hallucinate

Large language models fundamentally operate by predicting the next token in a sequence, a process that can lead to plausible but incorrect outputs, a phenomenon known as hallucination. Computer science professor Dan Klein likens this core function to speech recognition, where models assign probabilities to potential next words. While the transformer architecture, common in models like Anthropic's Claude and Meta's Llama 3, allows tokens to draw on previous context, its quadratic scaling with sequence length increases costs for longer prompts. Developers should treat LLM outputs as plausible samples requiring validation, keep prompts concise, and be aware that base models lack inherent instruction-following capabilities, necessitating post-training for assistant-like behavior. AI

IMPACT Understanding LLM token prediction and hallucination is crucial for developers to build more reliable AI applications.

RANK_REASON Article explains the fundamental workings of LLMs and their limitations, drawing on expert opinion and research papers, rather than announcing a new release or product.

Read on dev.to — LLM tag →

• Anthropic
• Attention Is All You Need
• .claude
• Dan Klein
• Llama 3
• Scaled Cognition
• University of California, Berkeley

• model release

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