LLMs Get 'Lost in the Middle' of Long Contexts
LLMs struggle to find information buried in the middle of long prompts. Performance is highest when key facts are at the beginning or end of the context. This impacts multi-document QA and RAG.
Even LLMs built for long contexts get "lost in the middle." They show a U-shaped performance curve, recalling info best from the beginning or end of a prompt but struggling with facts buried deep inside. This is critical for multi-document QA and RAG systems where the key detail might be anywhere. The footgun is assuming a large context window means uniform attention; it doesn't. For best results, place critical information at the start or end of the context.
Read the original → aclanthology.org
- #llm
- #ai
- #prompt engineering
- #context window
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