Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering
Authors: Zhenyun Deng, Yonghua Zhu, Yang Chen, Michael Witbrock, Patricia Riddle
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Hotpot QA demonstrate that our approach is competitive for interpretable reasoning and that the sub-questions generated by QDAMR are well-formed, outperforming existing question-decomposition-based multihop QA approaches. |
| Researcher Affiliation | Academia | School of Computer Science, University of Auckland, New Zealand EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 : Question Decomposition Based on AMR |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of its source code. |
| Open Datasets | Yes | Given ordered sub-questions, we train a single-hop QA on SQUAD [Rajpurkar et al., 2016] and on the new single-hop QA dataset. The dataset consists of single-hop QA pairs constructed by [Pan et al., 2020] on Hotpot QA. |
| Dataset Splits | Yes | Table 1: Results for QD-based multi-hop QA models on the dev set of Hotpot QA. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like BART, T5, Spring, RoBERTa, but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | No | The paper describes the general experimental process but does not provide specific hyperparameters (e.g., learning rate, batch size) or detailed system-level training settings in the main text. |