Entailed Between the Lines: Incorporating Implication into NLI
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.
Code (1)
Tasks
Natural Language InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrieval
Natural language inference has trended toward studying contexts beyond the sentence level. An important application area is law: past cases often do not foretell how they apply to new situations and implications must be …
Domain GeneralizationNatural Language InferenceRe-RankingRetrieval+1Multimodal Semantic Simulations of Linguistically Underspecified Motion Events
In this paper, we describe a system for generating three-dimensional visual simulations of natural language motion expressions. We use a rich formal model of events and their participants to generate simulations that sat…
Learning Independent Causal Mechanisms
Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeling, the structure of each distribution i…
Transfer LearningRevising Image-Text Retrieval via Multi-Modal Entailment
An outstanding image-text retrieval model depends on high-quality labeled data. While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent…
Image-text RetrievalNatural Language InferenceRetrievalSentence+1DELE: Deductive $\mathcal{EL}^{++} \thinspace $ Embeddings for Knowledge Base Completion
Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Descripti…
Knowledge Base CompletionOntology Embedding