Does GPT-4's multi-hop reasoning accuracy on HotpotQA degrade monotonically with increasing retrieval steps (2

Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn, especially when embedded in more complex tasks. To address this, we propose Decomposed Prompting, a new approach to solve complex tasks by decomposing them (via prompting) into simpler sub-tasks that can be delegated to a library of prompting-based LLMs dedicated to these sub-tasks. This modular structure allows each prompt to be optimized f

Research goal: Does GPT-4's multi-hop reasoning accuracy on HotpotQA degrade monotonically with increasing retrieval steps (2 vs 5) under controlled context length, and how does the accuracy-throughput trade-off compare against a single-step retrieval with wider context window?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.