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 351–400 of 1961 papers

TitleStatusHype
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical TasksCode2
Drafting Event Schemas using Language Models—0
Contrastive Learning of Sentence Embeddings from ScratchCode1
Prompting Large Language Models for Counterfactual Generation: An Empirical Study—0
Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual EntailmentCode0
Sources of Hallucination by Large Language Models on Inference TasksCode1
Can Large Language Models Capture Dissenting Human Voices?Code0
Evaluating and Modeling Attribution for Cross-Lingual Question Answering—0
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-TuningCode2
REFinD: Relation Extraction Financial DatasetCode0
Sentence Representations via Gaussian EmbeddingCode0
Atomic Inference for NLI with Generated Facts as AtomsCode0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual VerbalizerCode0
RWKV: Reinventing RNNs for the Transformer EraCode6
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing—0
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical ReasoningCode1
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity AlignmentCode0
Solving NLP Problems through Human-System Collaboration: A Discussion-based ApproachCode0
PDP: Parameter-free Differentiable Pruning is All You Need—0
TrueTeacher: Learning Factual Consistency Evaluation with Large Language ModelsCode4
PaLM 2 Technical Report—0
Adapting Sentence Transformers for the Aviation Domain—0
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative ExamplesCode0
ZARA: Improving Few-Shot Self-Rationalization for Small Language ModelsCode0
Automatic Evaluation of Attribution by Large Language ModelsCode1
MoT: Memory-of-Thought Enables ChatGPT to Self-ImproveCode1
NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial ReportsCode1
SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data—0
Stance Detection: A Practical Guide to Classifying Political Beliefs in TextCode1
Prompting for explanations improves Adversarial NLI. Is this true? Yes it is true because it weakens superficial cues—0
POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained modelsCode1
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial ReportsCode0
Compressing Sentence Representation with maximum Coding Rate Reduction—0
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation—0
ReCEval: Evaluating Reasoning Chains via Correctness and InformativenessCode1
Interventional Probing in High Dimensions: An NLI Case Study—0
Learning Homographic Disambiguation Representation for Neural Machine Translation—0
Uncertainty-Aware Natural Language Inference with Stochastic Weight AveragingCode0
Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language UnderstandingCode1
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4Code1
ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about—0
Unsupervised Improvement of Factual Knowledge in Language ModelsCode0
GreekBART: The First Pretrained Greek Sequence-to-Sequence ModelCode0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
BloombergGPT: A Large Language Model for Finance—0
Zero-shot Entailment of Leaderboards for Empirical AI Research—0
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization—0
Natural Language Reasoning, A SurveyCode1
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All datasetsSNLIRTEMultiNLIQNLIANLI testWNLILiDiRusRCBTERRaCommitmentBankSciTailFarsTail

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7—Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5—Unverified
3Neural Tree Indexers for Text Understanding% Test Accuracy93.1—Unverified
4EFL (Entailment as Few-shot Learner) + RoBERTa-large% 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 11B (fine-tuned)Accuracy92.5—Unverified
9T5-XXL 11BAccuracy92.5—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
8Adv-RoBERTa ensembleMatched91.1—Unverified
9DeBERTa (large)Matched91.1—Unverified
10SMARTRoBERTaDev Matched91.1—Unverified