@inproceedings{neves-ribeiro-etal-2022-entailment,
title = "Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner",
author = "Neves Ribeiro, Danilo and
Wang, Shen and
Ma, Xiaofei and
Dong, Rui and
Wei, Xiaokai and
Zhu, Henghui and
Chen, Xinchi and
Xu, Peng and
Huang, Zhiheng and
Arnold, Andrew and
Roth, Dan",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.35",
doi = "10.18653/v1/2022.findings-naacl.35",
pages = "465--475",
abstract = "Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system{'}s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300{\%} gain in overall correctness.",
}
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<abstract>Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system’s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.</abstract>
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%0 Conference Proceedings
%T Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
%A Neves Ribeiro, Danilo
%A Wang, Shen
%A Ma, Xiaofei
%A Dong, Rui
%A Wei, Xiaokai
%A Zhu, Henghui
%A Chen, Xinchi
%A Xu, Peng
%A Huang, Zhiheng
%A Arnold, Andrew
%A Roth, Dan
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F neves-ribeiro-etal-2022-entailment
%X Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system’s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.
%R 10.18653/v1/2022.findings-naacl.35
%U https://aclanthology.org/2022.findings-naacl.35
%U https://doi.org/10.18653/v1/2022.findings-naacl.35
%P 465-475
Markdown (Informal)
[Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner](https://aclanthology.org/2022.findings-naacl.35) (Neves Ribeiro et al., Findings 2022)
ACL
- Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, and Dan Roth. 2022. Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 465–475, Seattle, United States. Association for Computational Linguistics.