# Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network

Source: [Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network](<https://www.sota2.com/research/paper/68fa780b1e209131cea767db>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:30:26.676Z

Authors: Guanming Xiong

Published: 2020-04-20T09:34:16.000Z

## Abstract

In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of documents. The reasoning process is convert to node classify task(i.e., paragraph nodes and sentences nodes). The second stage is a language model fine-tuning task. In a word, stage one use graph neural network to select and concatenate support sentences as one paragraph, and stage two find the answer span in language model fine-tuning paradigm.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.13821>)

[PDF](<https://arxiv.org/pdf/2004.13821.pdf>)