When Your LLM Starts Bleeding Context (And How I Fixed It)
SMRTR summary
LLMs processing data in batches often suffer from "context bleeding," where previous entries influence current predictions, causing accuracy to plummet from 91% to 76% in edge cases. The solution involves linearizing tabular data into explicit key-value pairs and using few-shot prompting, which reduces error rates to under 15%.
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