Step Feasibility-Aware and Error-Correctable Entailment Tree Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for generating entailment trees suffer from false feasible steps, resulting in error propagation. |
| Approach: | They propose an iterative entailment tree generation framework with step feasibility perception and state error handling mechanisms to enhance the interpretability of QA systems. |
| Outcome: | The proposed framework improves the interpretation of QA systems by demonstrating that it is feasible to choose steps that are false feasible and error propagating. |
Similar Papers
FRVA: Fact-Retrieval and Verification Augmented Entailment Tree Generation for Explainable Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for generating a entailment tree exhibit the reasoning chains from knowledge facts to predicted answers, but they have large fact search spaces and error accumulation problems resulting in the generation of invalid steps. |
| Approach: | They propose a Fact-Retrieval and Verification Augmented bidirectional entailment tree generation method that contains two systems. |
| Outcome: | The proposed method outperforms existing models and achieves state-of-the-art performance in fact selection and structural correctness. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
Copied to clipboard
Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing work on QA explanation proposes to explain the answers with entailment trees composed of multiple enlargement steps. |
| Approach: | They propose a Module-based Entailment Tree GENeration framework that has multiple modules and a reasoning controller. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the standard benchmark with only 9% of the parameters. |
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing structured reasoning frameworks lack internal decision probability and cannot model the tree as a whole. |
| Approach: | They propose a Reinforcement Learning based Entailment Tree generation framework that is trained using the cumulative signals across the whole tree. |
| Outcome: | The proposed framework offers explicit deductions with entailment steps in a tree structure. |
Natural Logic at the Core: Dynamic Rewards for Entailment Tree Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to generating entailment trees lack logical consistency . static reward structures or intricate dependencies within multi-step reasoning are often ignored . |
| Approach: | They propose a method that integrates natural logic principles into reinforcement learning to guide entailment tree generation. |
| Outcome: | Experiments on EntailmentBank show that the proposed method improves interpretability and generalization. |
Explaining Answers with Entailment Trees (2021.emnlp-main)
Copied to clipboard
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, Peter Clark
| Challenge: | ENTAILMENTBANK is the first dataset to contain multistep entailment trees. |
| Approach: | They propose to generate explanations in the form of entailment trees, a tree of multipremise entanglements steps from facts that are known to the hypothesis of interest. |
| Outcome: | The proposed model can generate explanations in the form of entailment trees . this is a tree of multipremise enttailment steps from facts known to the hypothesis of interest. |
Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for science question answering lack a framework for entailment trees . ambiguities and similarities between science facts complicate the fact retrieval process . |
| Approach: | They propose a framework for building entailment trees for science question answering . they propose to infuse knowledge that bridges the gap between reasoning types and rhetorical relations . |
| Outcome: | The proposed framework improves retrieval capabilities, understanding relationships and generating intermediate conclusions. |
Stepwise Informativeness Search for Improving LLM Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models have improved multistep reasoning but they lose focus over the middle of long contexts. |
| Approach: | They propose a tree search framework that proactively identifies underutilized steps and minimizing redundant information between steps. |
| Outcome: | The proposed framework generates more accurate and concise rationales with reduced errors and redundancy. |
From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to construct entailment graphs suffer from severe sparsity issues due to limited corpora and the long-tail phenomenon of predicate distributions. |
| Approach: | They propose a multi-stage method to generate entailment graphs by generating new predicates and detecting enanglement relations among seed predicats. |
| Outcome: | The proposed method can generate high-quality graphs with high precision over state-of-the-art methods and boost the performance of down-stream inference tasks. |
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to improve mathematical reasoning require extensive datasets for training or depend on few-shot methods that compromise computational accuracy. |
| Approach: | They propose a training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities. |
| Outcome: | The proposed framework outperforms Qwen2.5-72B-Math-Instruct on MMLU-STEM with a score of 90.9%, compared to 87.3%. |