A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. |
| Approach: | They propose a model that utilizes logical entailment patterns to generate coherent explanations by leveraging logical patterns. |
| Outcome: | The proposed model produces more coherent and reasonable conclusions that closely align with the underlying premises. |
Similar Papers
Abstract-level Deductive Reasoning for Pre-trained Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods fine-tune PLMs using the validity label and instance-level reasoning proofs as supervision signals. |
| Approach: | They propose to train PLMs to learn general reasoning patterns rather than instance-level knowledge by predicting the abstract reasoning proof of each sample. |
| Outcome: | The proposed model significantly reduces the impact of learning instance-level knowledge (over 70%) |
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. |
Logical Natural Language Generation from Open-Domain Tables (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies on neural natural language generation focus on surface-level realizations with limited emphasis on logical inference. |
| Approach: | They propose a task where a model is tasked with generating natural language statements that can be logically entailed by facts in an open-domain semi-structured table. |
| Outcome: | The proposed task is based on the existing TabFact dataset with a wide range of logical/symbolic inferences. |
From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning (2026.eacl-srw)
Copied to clipboard
| Challenge: | Multi-hop reasoning requires a chain of facts to reflect the reasoning behind the answer. |
| Approach: | They propose an inference-guided prompting approach that performs well in natural language questions . they propose a neuro-symbolic approach to reasoning using large language models . |
| Outcome: | The proposed model outperforms all prompting strategies and fine-tunes LLMs trained specifically for proof generation. |
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. |
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. |
Bridging Knowledge Gaps in Neural Entailment via Symbolic Models (D18-1)
Copied to clipboard
| Challenge: | Textual entailment models focus on lexical gaps but rarely on knowledge gaps. |
| Approach: | They propose a fact-level decomposition of the hypothesis and a knowledge lookup module to fill knowledge gaps in Science Entailment task. |
| Outcome: | The proposed model outperforms the base model on the SciTail dataset by 3% and 5% on the textual premise and the structured knowledge base. |
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. |
Flexible Generation of Natural Language Deductions (2021.emnlp-main)
Copied to clipboard
| Challenge: | Developing models that can make useful inferences from natural language premises has been a core goal in artificial intelligence since the field's early days. |
| Approach: | They propose a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision. |
| Outcome: | The proposed model is more accurate and flexible than baseline systems. |
Exploring Graph Representations of Logical Forms for Language Modeling (2025.findings-acl)
Copied to clipboard
| Challenge: | Graph-based formal-logical distributional semantics models are more data-efficient than textual counterparts. |
| Approach: | They propose a pretrained language model over graph representations of logical forms as a proof-of-concept. |
| Outcome: | The proposed model outperforms textual, transformer LMs on downstream tasks . the model is likely to scale with additional parameters and pretraining data . |