# Multi-Hop Question Answering: When Can Humans Help, and Where do They Struggle?

arXiv: https://arxiv.org/abs/2510.04493 · HTML: https://mlsanity.com/paper/2510.04493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- First posted on arXiv 6 Oct 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Built on by (most cited first, 1 of 1)

- [THOR: A Theta-Gamma Hierarchical Oscillatory Reasoning Framework for Multi-hop QA](https://mlsanity.com/paper/2607.20459/) (2026)

## Abstract (arXiv)

Multi-hop question answering is a challenging task for both large language models (LLMs) and humans, as it requires recognizing when multi-hop reasoning is needed, followed by reading comprehension, logical reasoning, and knowledge integration. To better understand how humans might collaborate effectively with AI, we evaluate the performance of crowd workers on these individual reasoning subtasks. We find that while humans excel at knowledge integration (97% accuracy), they often fail to recognize when a question requires multi-hop reasoning (67% accuracy). Participants perform reasonably well on both single-hop and multi-hop QA (84% and 80% accuracy, respectively), but frequently make semantic mistakes--for example, answering "when" an event happened when the question asked "where." These findings highlight the importance of designing AI systems that complement human strengths while compensating for common weaknesses.