If you're hitting degraded quality on long-document tasks, this shows a planner/worker/manager decomposition that lets cheaper open models like Llama-3-70B and Qwen-72B beat single-shot GPT-4o — a practical way to cut cost while improving reliability on large contexts.
As context windows grow, LLM performance degrades in unexpected ways.
We show how a "Divide & Conquer" framework — breaking long documents into parallel chunks with a planner, workers, and manager — lets smaller models like Llama-3-70B and Qwen-72B outperform GPT-4o single-shot.
Transcript
As context windows grow, LLM performance degrades in unexpected ways. We show how a "Divide & Conquer" framework — breaking long documents into parallel chunks with a planner, workers, and manager — lets smaller models like Llama-3-70B and Qwen-72B outperform GPT-4o single-shot.
