# Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games

arXiv: https://arxiv.org/abs/2004.02393 · HTML: https://mlsanity.com/paper/2004.02393/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge](https://mlsanity.com/paper/2412.17032/) (2024)
- [Reasoning over Hybrid Chain for Table-and-Text Open Domain QA](https://mlsanity.com/paper/2201.05880/) (2022)

## Abstract (arXiv)

We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and how the selected passages are connected are handled by two models that cooperate to select the most confident chains from a large set of candidates (from distant supervision). For evaluation, we created benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and hand-labeled reasoning chains for the latter. The experimental results demonstrate the effectiveness of our proposed approach.