# Entailed Between the Lines: Incorporating Implication into NLI

arXiv: https://arxiv.org/abs/2501.07719 · HTML: https://mlsanity.com/paper/2501.07719/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 27 of 27)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Hypothesis Only Baselines in Natural Language Inference](https://mlsanity.com/paper/1805.01042/) (2018)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://mlsanity.com/paper/2201.05955/) (2022)
- [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://mlsanity.com/paper/2004.03066/) (2020)
- [NormBank: A Knowledge Bank of Situational Social Norms](https://mlsanity.com/paper/2305.17008/) (2023)
- ["I'd rather just go to bed": Understanding Indirect Answers](https://mlsanity.com/paper/2010.03450/) (2020)
- [Ordinal Common-sense Inference](https://mlsanity.com/paper/1611.00601/) (2016)
- [COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements](https://mlsanity.com/paper/2306.01985/) (2023)
- [JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims](https://mlsanity.com/paper/2401.08026/) (2024)
- [Regularization techniques for fine-tuning in neural machine translation](https://mlsanity.com/paper/1707.09920/) (2017)
- [A synthetic data approach for domain generalization of NLI models](https://mlsanity.com/paper/2402.12368/) (2024)
- [Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom](https://mlsanity.com/paper/2404.19509/) (2024)
- [Conversational implicatures in English dialogue: Annotated dataset](https://mlsanity.com/paper/1911.10704/) (2019)
- [Scalable and Domain-General Abstractive Proposition Segmentation](https://mlsanity.com/paper/2406.19803/) (2024)
- [Resolving Indirect Referring Expressions for Entity Selection](https://mlsanity.com/paper/2212.10933/) (2022)
- [Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders](https://mlsanity.com/paper/2403.04314/) (2024)

## Built on by (most cited first, 9 of 9)

- [Culturally-Aware Conversations: A Framework & Benchmark for LLMs](https://mlsanity.com/paper/2510.11563/) (2025)
- [Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection](https://mlsanity.com/paper/2505.20209/) (2025)
- [T-FIX: Text-Based Explanations with Features Interpretable to eXperts](https://mlsanity.com/paper/2511.04070/) (2025)
- [On Reference (In-)Determinacy in Natural Language Inference](https://mlsanity.com/paper/2502.05793/) (2025)
- [Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference](https://mlsanity.com/paper/2508.14735/) (2025)
- [Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisions](https://mlsanity.com/paper/2603.22973/) (2026)
- [One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations](https://mlsanity.com/paper/2510.09293/) (2025)
- [Explanation Generation for Contradiction Reconciliation with LLMs](https://mlsanity.com/paper/2603.22735/) (2026)
- [Reference-Free Evaluation of Reasoning in Open-Ended Question Answering](https://mlsanity.com/paper/2607.19678/) (2026)

## Abstract (arXiv)

Much of human communication depends on implication, conveying meaning beyond literal words to express a wider range of thoughts, intentions, and feelings. For models to better understand and facilitate human communication, they must be responsive to the text's implicit meaning. We focus on Natural Language Inference (NLI), a core tool for many language tasks, and find that state-of-the-art NLI models and datasets struggle to recognize a range of cases where entailment is implied, rather than explicit from the text. We formalize implied entailment as an extension of the NLI task and introduce the Implied NLI dataset (INLI) to help today's LLMs both recognize a broader variety of implied entailments and to distinguish between implicit and explicit entailment. We show how LLMs fine-tuned on INLI understand implied entailment and can generalize this understanding across datasets and domains.