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

Showing 201250 of 1961 papers

TitleStatusHype
MetaICL: Learning to Learn In ContextCode1
A Statistical Framework for Low-bitwidth Training of Deep Neural NetworksCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
A Structured Self-attentive Sentence EmbeddingCode1
Model Editing Harms General Abilities of Large Language Models: Regularization to the RescueCode1
A Study of Situational Reasoning for Traffic UnderstandingCode1
Enhancing Clinical BERT Embedding using a Biomedical Knowledge BaseCode1
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature PerspectiveCode1
A Surprisingly Robust Trick for Winograd Schema ChallengeCode1
Deep contextualized word representationsCode1
Adversarial Filters of Dataset BiasesCode1
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
mT5: A massively multilingual pre-trained text-to-text transformerCode1
MT-Ranker: Reference-free machine translation evaluation by inter-system rankingCode1
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark DatasetsCode1
Natural Language Reasoning, A SurveyCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Entailment as Few-Shot LearnerCode1
NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial ReportsCode1
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-AttentionCode1
Differentiable Subset Pruning of Transformer HeadsCode1
On Faithfulness and Factuality in Abstractive SummarizationCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
EENLP: Cross-lingual Eastern European NLP IndexCode1
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question AnsweringCode1
Selective Generation for Controllable Language ModelsCode1
Augmenting Transformers with Recursively Composed Multi-grained RepresentationsCode1
e-SNLI: Natural Language Inference with Natural Language ExplanationsCode1
ParsiNLU: A Suite of Language Understanding Challenges for PersianCode1
Pay Attention to MLPsCode1
Empowering Language Understanding with Counterfactual ReasoningCode1
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionCode1
Automatic Evaluation of Attribution by Large Language ModelsCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Distributed NLI: Learning to Predict Human Opinion Distributions for Language ReasoningCode1
Distributionally Robust Neural NetworksCode1
Enhanced LSTM for Natural Language InferenceCode1
DocNLI: A Large-scale Dataset for Document-level Natural Language InferenceCode1
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in BanglaCode1
Q8BERT: Quantized 8Bit BERTCode1
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated PromptsCode1
When Do Flat Minima Optimizers Work?Code1
Analyzing Multi-Task Learning for Abstractive Text SummarizationCode1
RoBERTa: A Robustly Optimized BERT Pretraining ApproachCode1
An Empirical Study of Pre-trained Transformers for Arabic Information ExtractionCode1
Don't Say No: Jailbreaking LLM by Suppressing RefusalCode1
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded HallucinationsCode1
Enhancing adversarial robustness in Natural Language Inference using explanationsCode1
SciFive: a text-to-text transformer model for biomedical literatureCode1
e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language ExplanationsCode1
Show:102550
← PrevPage 5 of 40Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5Unverified
3EFL (Entailment as Few-shot Learner) + RoBERTa-large% Test Accuracy93.1Unverified
4Neural Tree Indexers for Text Understanding% Test Accuracy93.1Unverified
5RoBERTa-large+Self-Explaining% Test Accuracy92.3Unverified
6RoBERTa-large + self-explaining layer% Test Accuracy92.3Unverified
7CA-MTL% Test Accuracy92.1Unverified
8SemBERT% Test Accuracy91.9Unverified
9MT-DNN-SMARTLARGEv0% Test Accuracy91.7Unverified
10MT-DNN-SMART_100%ofTrainingDataDev Accuracy91.6Unverified
#ModelMetricClaimedVerifiedStatus
1Vega v2 6B (KD-based prompt transfer)Accuracy96Unverified
2PaLM 540B (fine-tuned)Accuracy95.7Unverified
3Turing NLR v5 XXL 5.4B (fine-tuned)Accuracy94.1Unverified
4ST-MoE-32B 269B (fine-tuned)Accuracy93.5Unverified
5DeBERTa-1.5BAccuracy93.2Unverified
6MUPPET Roberta LargeAccuracy92.8Unverified
7DeBERTaV3largeAccuracy92.7Unverified
8T5-XXL 11BAccuracy92.5Unverified
9T5-XXL 11B (fine-tuned)Accuracy92.5Unverified
10ST-MoE-L 4.1B (fine-tuned)Accuracy92.1Unverified
#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)Matched92.6Unverified
2Turing NLR v5 XXL 5.4B (fine-tuned)Matched92.6Unverified
3T5-XXL 11B (fine-tuned)Matched92Unverified
4T5Matched92Unverified
5T5-11BMismatched91.7Unverified
6T5-3BMatched91.4Unverified
7ALBERTMatched91.3Unverified
8DeBERTa (large)Matched91.1Unverified
9Adv-RoBERTa ensembleMatched91.1Unverified
10SMARTRoBERTaDev Matched91.1Unverified