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
Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns—0
DISTRIBUTIONALLY ROBUST NEURAL NETWORKS FOR GROUP SHIFTS ON THE IMPORTANCE OF REGULARIZATION FOR WORST-CASE GENERALIZATION—0
RuMedBench: A Russian Medical Language Understanding BenchmarkCode1
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
On Measuring Social Biases in Prompt-Based Learning—0
LawngNLI: a multigranular, long-premise NLI benchmark for evaluating models’ in-domain generalization from short to long contexts—0
DialogueScore: Evaluating Responses in Task-Oriented Dialogue—0
IMPLI: Investigatng NLI Models' Performance on Figurative Language—0
A Multilingual Perspective Towards the Evaluation of Attribution Methods—0
Practical Dataless Text Classification Through Dense Retrieval—0
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization—0
MetaICL: Learning to Learn In Context—0
Framework for Weakly Supervised Causal Knowledge Extraction from Text—0
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?—0
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset CreationCode1
Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies—0
Explaining Predictive Uncertainty by Looking Back at Model Explanations—0
SCROLLS: Standardized CompaRison Over Long Language SequencesCode1
Polish Natural Language Inference and Factivity -- an Expert-based Dataset and Benchmarks—0
New Methods & Metrics for LFQA tasks—0
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?Code1
Few-shot Learning with Multilingual Language ModelsCode1
Analysis and Mitigation of Dataset Artifacts in OpenAI GPT-3—0
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers—0
Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun PhrasesCode0
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant SetupCode0
A Puzzle-Based Dataset for Natural Language InferenceCode0
DIBERT: Dependency Injected Bidirectional Encoder Representations from TransformersCode0
LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI—0
Inducing Causal Structure for Interpretable Neural NetworksCode1
Interactive Model with Structural Loss for Language-based Abductive Reasoning—0
DRONE: Data-aware Low-rank Compression for Large NLP Models—0
IRM---when it works and when it doesn't: A test case of natural language inference—0
FastTrees: Parallel Latent Tree-Induction for Faster Sequence EncodingCode0
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingCode2
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in SummarizationCode1
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
Prompt-Learning for Fine-Grained Entity Typing—0
Can Pre-trained Models Really Generate Single-Step Textual Entailment?—0
Do Current Natural Language Inference Models Truly Understand Sentences? Insights from Simple Sentences—0
Textual Entailment with Dynamic Contrastive Learning for Zero-shot NER—0
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation—0
Causal Transformers: Improving the Robustness on Spurious Correlations—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
Can Explanations Be Useful for Calibrating Black Box Models?—0
Lifting the Curse of Multilinguality by Pre-training Modular Transformers—0
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets—0
Persian Natural Language Inference: A Meta-learning approach—0
Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling—0
Show:102550
← PrevPage 14 of 40Next →
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