Does GPT-4's multi-hop reasoning accuracy on HotpotQA degrade monotonically with increasing retrieval steps (2 - Assignee Research (Assignee Research, 2026)
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-
Machine-generated literature synthesis.