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 251–300 of 1961 papers

TitleStatusHype
Can Explanations Be Useful for Calibrating Black Box Models?Code1
FarsTail: A Persian Natural Language Inference DatasetCode1
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in BanglaCode1
Exploring the Benefits of Training Expert Language Models over Instruction TuningCode1
Few-shot Learning with Multilingual Language ModelsCode1
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
Building Efficient Universal Classifiers with Natural Language InferenceCode1
FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop DataCode1
Calibration of Pre-trained TransformersCode1
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
Fast and Accurate Factual Inconsistency Detection Over Long DocumentsCode1
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP TasksCode1
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Finding a Balanced Degree of Automation for Summary EvaluationCode1
Are self-explanations from Large Language Models faithful?Code1
Can NLI Models Verify QA Systems’ Predictions?Code1
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data GenerationCode1
From English To Foreign Languages: Transferring Pre-trained Language ModelsCode1
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performanceCode1
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTCode1
A Broad-Coverage Challenge Corpus for Sentence Understanding through InferenceCode1
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n ParametersCode1
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded HallucinationsCode1
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From CharactersCode1
Charformer: Fast Character Transformers via Gradient-based Subword TokenizationCode1
Big Bird: Transformers for Longer SequencesCode1
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersCode1
Few-Shot Learning with Siamese Networks and Label TuningCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Citation-Enhanced Generation for LLM-based ChatbotsCode1
BioELECTRA:Pretrained Biomedical text Encoder using DiscriminatorsCode1
Improving Language Understanding by Generative Pre-TrainingCode1
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencesCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
MASKER: Masked Keyword Regularization for Reliable Text ClassificationCode1
Compositional Explanations of NeuronsCode1
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat ViolenceCode1
Compositional Exemplars for In-context LearningCode1
Compositional Evaluation on Japanese Textual Entailment and SimilarityCode1
InfoBERT: Improving Robustness of Language Models from An Information Theoretic PerspectiveCode1
RealFormer: Transformer Likes Residual AttentionCode1
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less DataCode1
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language InferenceCode1
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen DomainsCode1
ContractNLI: A Dataset for Document-level Natural Language Inference for ContractsCode1
KLUE: Korean Language Understanding EvaluationCode1
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
A Generalized Framework of Sequence Generation with Application to Undirected Sequence ModelsCode0
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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 layer% Test Accuracy92.3—Unverified
6RoBERTa-large+Self-Explaining% 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 11B (fine-tuned)Accuracy92.5—Unverified
9T5-XXL 11BAccuracy92.5—Unverified
10UL2 20B (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
8Adv-RoBERTa ensembleMatched91.1—Unverified
9DeBERTa (large)Matched91.1—Unverified
10SMARTRoBERTaDev Matched91.1—Unverified