SOTAVerified

Natural Language Inference

Natural language inference (NLI) is the task of determining whether a "hypothesis" is true (entailment), false (contradiction), or undetermined (neutral) given a "premise".

Example:

| Premise | Label | Hypothesis | | --- | ---| --- | | A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. | | An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. | | A soccer game with multiple males playing. | entailment | Some men are playing a sport. |

Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include SNLI, MultiNLI, SciTail, among others. You can get hands-on practice on the SNLI task by following this d2l.ai chapter.

Further readings:

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1650 of 1961 papers

TitleStatusHype
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization—0
FarFetched: An Entity-centric Approach for Reasoning on Textually Represented Environments—0
FBK: Cross-Lingual Textual Entailment Without Translation—0
FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTE—0
FBK-UEdin Participation to the WMT13 Quality Estimation Shared Task—0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models—0
Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment—0
Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation—0
Feedforward Legendre Memory Unit—0
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label—0
Few-Shot Learning with Siamese Networks and Label Tuning—0
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration—0
Filtrage et régularisation pour améliorer la plausibilité des poids d’attention dans la tâche d’inférence en langue naturelle (Filtering and regularization to improve the plausibility of attention weights in NLI)—0
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?—0
Fine-grained Semantic Textual Similarity for Serbian—0
Fine-tune BERT with Sparse Self-Attention Mechanism—0
First steps towards a Predicate Matrix—0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI—0
FLamE: Few-shot Learning from Natural Language Explanations—0
Focus Annotation in Reading Comprehension Data—0
Focused Entailment Graphs for Open IE Propositions—0
Formal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inference—0
FrameNet+: Fast Paraphrastic Tripling of FrameNet—0
Framework for Weakly Supervised Causal Knowledge Extraction from Text—0
From Superficial Patterns to Semantic Understanding: Fine-Tuning Language Models on Contrast Sets—0
From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items—0
From Textual Entailment to Knowledgeable Machines—0
Fusion of Compositional Network-based and Lexical Function Distributional Semantic Models—0
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index—0
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generating Contradictory, Neutral, and Entailing Sentences—0
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets—0
Generating Intermediate Steps for NLI with Next-Step Supervision—0
Generating Natural Language Inference Chains—0
Generating Pattern-Based Entailment Graphs for Relation Extraction—0
Generating Token-Level Explanations for Natural Language Inference—0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading—0
Giving AI Personalities Leads to More Human-Like Reasoning—0
GKR: the Graphical Knowledge Representation for semantic parsing—0
Global Inference for Bridging Anaphora Resolution—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
GLUECoS : An Evaluation Benchmark for Code-Switched NLP—0
GLUECoS: An Evaluation Benchmark for Code-Switched NLP—0
Good, Great, Excellent: Global Inference of Semantic Intensities—0
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation—0
Show:102550
← PrevPage 33 of 40Next →
All datasetsSNLIRTEMultiNLIQNLIANLI testWNLILiDiRusRCBTERRaCommitmentBankSciTailFarsTail

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7—Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5—Unverified
3EFL (Entailment as Few-shot Learner) + RoBERTa-large% Test Accuracy93.1—Unverified
4Neural Tree Indexers for Text Understanding% Test Accuracy93.1—Unverified
5RoBERTa-large+Self-Explaining% Test Accuracy92.3—Unverified
6RoBERTa-large + self-explaining layer% Test Accuracy92.3—Unverified
7CA-MTL% Test Accuracy92.1—Unverified
8SemBERT% Test Accuracy91.9—Unverified
9MT-DNN-SMARTLARGEv0% Test Accuracy91.7—Unverified
10MT-DNN-SMART_100%ofTrainingDataDev Accuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Vega v2 6B (KD-based prompt transfer)Accuracy96—Unverified
2PaLM 540B (fine-tuned)Accuracy95.7—Unverified
3Turing NLR v5 XXL 5.4B (fine-tuned)Accuracy94.1—Unverified
4ST-MoE-32B 269B (fine-tuned)Accuracy93.5—Unverified
5DeBERTa-1.5BAccuracy93.2—Unverified
6MUPPET Roberta LargeAccuracy92.8—Unverified
7DeBERTaV3largeAccuracy92.7—Unverified
8T5-XXL 11BAccuracy92.5—Unverified
9T5-XXL 11B (fine-tuned)Accuracy92.5—Unverified
10ST-MoE-L 4.1B (fine-tuned)Accuracy92.1—Unverified
#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)Matched92.6—Unverified
2Turing NLR v5 XXL 5.4B (fine-tuned)Matched92.6—Unverified
3T5-XXL 11B (fine-tuned)Matched92—Unverified
4T5Matched92—Unverified
5T5-11BMismatched91.7—Unverified
6T5-3BMatched91.4—Unverified
7ALBERTMatched91.3—Unverified
8DeBERTa (large)Matched91.1—Unverified
9Adv-RoBERTa ensembleMatched91.1—Unverified
10SMARTRoBERTaDev Matched91.1—Unverified