# Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner

arXiv: https://arxiv.org/abs/2205.09224 · HTML: https://mlsanity.com/paper/2205.09224/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 18 May 2022.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://mlsanity.com/paper/2012.13048/) (2020)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval](https://mlsanity.com/paper/2009.12756/) (2020)
- [Explaining Answers with Entailment Trees](https://mlsanity.com/paper/2104.08661/) (2021)
- [Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering](https://mlsanity.com/paper/2010.03274/) (2020)
- [Learning-to-Rank with BERT in TF-Ranking](https://mlsanity.com/paper/2004.08476/) (2020)
- [Flexible Generation of Natural Language Deductions](https://mlsanity.com/paper/2104.08825/) (2021)
- [Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval](https://mlsanity.com/paper/2104.05883/) (2021)
- [Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration](https://mlsanity.com/paper/2107.11879/) (2021)
- [Autoregressive Reasoning over Chains of Facts with Transformers](https://mlsanity.com/paper/2012.11321/) (2020)

## Built on by (most cited first, 26 of 26)

- [Faithful Reasoning Using Large Language Models](https://mlsanity.com/paper/2208.14271/) (2022)
- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://mlsanity.com/paper/2303.10868/) (2023)
- [RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation](https://mlsanity.com/paper/2403.05313/) (2024)
- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [Generating Natural Language Proofs with Verifier-Guided Search](https://mlsanity.com/paper/2205.12443/) (2022)
- [Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning](https://mlsanity.com/paper/2210.12217/) (2022)
- [Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving](https://mlsanity.com/paper/2405.01379/) (2024)
- [WikiWhy: Answering and Explaining Cause-and-Effect Questions](https://mlsanity.com/paper/2210.12152/) (2022)
- [NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning](https://mlsanity.com/paper/2209.07662/) (2022)
- [Faithful Question Answering with Monte-Carlo Planning](https://mlsanity.com/paper/2305.02556/) (2023)
- [Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees](https://mlsanity.com/paper/2209.10492/) (2022)
- [STREET: A Multi-Task Structured Reasoning and Explanation Benchmark](https://mlsanity.com/paper/2302.06729/) (2023)
- [Natural Language Deduction with Incomplete Information](https://mlsanity.com/paper/2211.00614/) (2022)
- [Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://mlsanity.com/paper/2402.14798/) (2024)
- [RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees](https://mlsanity.com/paper/2210.17095/) (2022)
- [Deductive Additivity for Planning of Natural Language Proofs](https://mlsanity.com/paper/2307.02472/) (2023)
- [SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning](https://mlsanity.com/paper/2401.13246/) (2024)
- [A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation](https://mlsanity.com/paper/2403.06410/) (2024)
- [An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanism](https://mlsanity.com/paper/2412.05821/) (2024)
- [Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels](https://mlsanity.com/paper/2305.03793/) (2023)
- [Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning](https://mlsanity.com/paper/2305.00061/) (2023)
- [From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering](https://mlsanity.com/paper/2412.17701/) (2024)
- [HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering](https://mlsanity.com/paper/2509.09713/) (2025)
- [Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives](https://mlsanity.com/paper/2208.01376/) (2022)
- [LogicPrpBank: A Corpus for Logical Implication and Equivalence](https://mlsanity.com/paper/2402.09609/) (2024)
- [Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation](https://mlsanity.com/paper/2409.17757/) (2024)

## Abstract (arXiv)

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 and inspect 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.