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 651–700 of 1961 papers

TitleStatusHype
PANACEA: An Automated Misinformation Detection System on COVID-19—0
Parallel Sentence-Level Explanation Generation for Real-World Low-Resource Scenarios—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough—0
For Generated Text, Is NLI-Neutral Text the Best Text?Code0
Language Model Analysis for Ontology Subsumption Inference—0
Investigating Multi-source Active Learning for Natural Language InferenceCode0
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations—0
Evaluating the Robustness of Discrete PromptsCode0
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks—0
Explanation Selection Using Unlabeled Data for Chain-of-Thought PromptingCode0
What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories—0
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution SimilaritiesCode0
Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response GenerationCode0
Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark DatasetsCode0
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language ModelsCode0
XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)—0
Universal Multimodal Representation for Language Understanding—0
Leveraging Semantic Representations Combined with Contextual Word Representations for Recognizing Textual Entailment in Vietnamese—0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction—0
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition—0
OpineSum: Entailment-based self-training for abstractive opinion summarization—0
WeCheck: Strong Factual Consistency Checker via Weakly Supervised LearningCode0
Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature—0
Cross-Lingual Retrieval Augmented Prompt for Low-Resource LanguagesCode0
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders—0
ALERT: Adapting Language Models to Reasoning Tasks—0
Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization—0
Towards Linguistically Informed Multi-Objective Pre-Training for Natural Language Inference—0
Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division—0
RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language UnderstandingCode0
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset—0
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based RetrievalCode0
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE—0
Utilizing Background Knowledge for Robust Reasoning over Traffic SituationsCode0
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
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
Validity Assessment of Legal Will Statements as Natural Language InferenceCode0
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models—0
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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