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 1801–1850 of 1961 papers

TitleStatusHype
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?Code0
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence BenchmarksCode0
AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysisCode0
Learning with Different Amounts of Annotation: From Zero to Many LabelsCode0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Utilizing Background Knowledge for Robust Reasoning over Traffic SituationsCode0
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI EvaluationCode0
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic ProgrammingCode0
Learning Natural Language Inference using Bidirectional LSTM model and Inner-AttentionCode0
Learning Natural Language Inference with LSTMCode0
Can Large Language Models Capture Dissenting Human Voices?Code0
Learning Semantic Textual Similarity from ConversationsCode0
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical WordsCode0
Learning the Difference that Makes a Difference with Counterfactually-Augmented DataCode0
Universal Evasion Attacks on Summarization ScoringCode0
ASCM: An Answer Space Clustered Prompting Method without Answer EngineeringCode0
Learning to Compose Task-Specific Tree StructuresCode0
ArNLI: Arabic Natural Language Inference for Entailment and Contradiction DetectionCode0
Learning to Distinguish Hypernyms and Co-HyponymsCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairsCode0
D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities of Large Language ModelsCode0
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language InferenceCode0
A Regularized Framework for Sparse and Structured Neural AttentionCode0
Learning to Model and Ignore Dataset Bias with Mixed Capacity EnsemblesCode0
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary SentenceCode0
Distilling Task-Specific Knowledge from BERT into Simple Neural NetworksCode0
Star-TransformerCode0
Learning to Select from Multiple OptionsCode0
Stochastic Answer Networks for Natural Language InferenceCode0
CamemBERT: a Tasty French Language ModelCode0
Story Ending Prediction by Transferable BERTCode0
Building a Dictionary of Affixal NegationsCode0
Stress Test Evaluation for Natural Language InferenceCode0
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label CorrelationCode0
Lessons from Natural Language Inference in the Clinical DomainCode0
A Hypothesis-Driven Framework for the Analysis of Self-Rationalising ModelsCode0
Leveraging Affirmative Interpretations from Negation Improves Natural Language UnderstandingCode0
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and UnderstandingCode0
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in ConversationCode0
Lexical Generalization Improves with Larger Models and Longer TrainingCode0
BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with TablesCode0
Stretching Sentence-pair NLI Models to Reason over Long Documents and ClustersCode0
Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning TasksCode0
WeCheck: Strong Factual Consistency Checker via Weakly Supervised LearningCode0
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data SelectionCode0
Quantifying Divergence for Human-AI Collaboration and Cognitive TrustCode0
Adverb Is the Key: Simple Text Data Augmentation with Adverb DeletionCode0
Lightweight Adaptation of Neural Language Models via Subspace EmbeddingCode0
Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language InferenceCode0
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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