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 451–500 of 1961 papers

TitleStatusHype
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
Utilizing Background Knowledge for Robust Reasoning over Traffic SituationsCode0
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE—0
Learning to Select from Multiple OptionsCode0
Revisiting text decomposition methods for NLI-based factuality scoring of summaries—0
Using Focal Loss to Fight Shallow Heuristics: An Empirical Analysis of Modulated Cross-Entropy in Natural Language InferenceCode0
TEMPERA: Test-Time Prompting via Reinforcement LearningCode1
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language InferenceCode0
Probing neural language models for understanding of words of estimative probability—0
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language InferenceCode0
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity TypingCode0
Logographic Information Aids Learning Better Representations for Natural Language Inference—0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization—0
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5Code0
Zero-Shot Text Classification with Self-TrainingCode1
Validity Assessment of Legal Will Statements as Natural Language InferenceCode0
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models—0
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI—0
BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification—0
MABEL: Attenuating Gender Bias using Textual Entailment DataCode1
Leveraging Affirmative Interpretations from Negation Improves Natural Language UnderstandingCode0
Analyzing Multi-Task Learning for Abstractive Text SummarizationCode1
BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial ExamplesCode0
OpenStance: Real-world Zero-shot Stance DetectionCode0
Universal Evasion Attacks on Summarization ScoringCode0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models—0
Conformal Predictor for Improving Zero-shot Text Classification Efficiency—0
Lexical Generalization Improves with Larger Models and Longer TrainingCode0
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning—0
R^2F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language InferenceCode0
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
Large Language Models Can Self-Improve—0
A Linguistic Investigation of Machine Learning based Contradiction Detection Models: An Empirical Analysis and Future Perspectives—0
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language—0
Textual Entailment Recognition with Semantic Features from Empirical Text Representation—0
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice PerspectiveCode4
ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive PretrainingCode0
Rethinking Annotation: Can Language Learners Contribute?—0
LIME: Weakly-Supervised Text Classification Without SeedsCode0
Assessing Out-of-Domain Language Model Performance from Few Examples—0
Benchmarking Long-tail Generalization with Likelihood SplitsCode0
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair ModelingCode0
Rethinking the Event Coding Pipeline with Prompt EntailmentCode0
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity RecognitionCode1
CORE: A Retrieve-then-Edit Framework for Counterfactual Data GenerationCode0
Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation—0
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal NegationCode0
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive SummarizationCode0
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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