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technologyreview.com·3h ago
AI Research
arXiv CS.AI·Jun 11·5 min read
From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
TL;DR
Large language models (LLMs) often produce inaccurate or nonsensical outputs, a phenomenon known as hallucination. This study investigates the structural origins of hallucination in LLMs, particularly focusing on the role of model architecture and training data.
✦ Why It Matters
Engineers can enhance LLM reliability by optimizing model architecture and carefully curating training data.
Key Takeaways
- Understand how self-attention contributes to entity confusion.
- Optimize training objectives to prioritize factual accuracy.
- Recognize the impact of autoregressive decoding on output errors.
- Assess dataset quality to reduce training biases.
How It Works
The paper explains that self-attention mechanisms in LLMs learn co-occurrences of words based on their statistical proximity, which can lead to confusion between similar entities. MLE training focuses on maximizing the likelihood of the next token, often at the expense of factual correctness.
Autoregressive decoding processes tokens sequentially, meaning that once an incorrect token is generated, it can affect all subsequent tokens, leading to a cascade of errors.