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 401–450 of 1961 papers

TitleStatusHype
Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Text—0
ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched EmbeddingsCode0
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?—0
Instruction Finetuning for Leaderboard Generation from Empirical AI Research—0
Towards a Generative Approach for Emotion Detection and Reasoning—0
Explicating the Implicit: Argument Detection Beyond Sentence Boundaries—0
Zero-shot Factual Consistency Evaluation Across DomainsCode0
Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data AugmentationCode0
Integrating Controllable Motion Skills from Demonstrations—0
Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings—0
Defining and Evaluating Decision and Composite Risk in Language Models Applied to Natural Language Inference—0
Leveraging Entailment Judgements in Cross-Lingual SummarisationCode0
Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning—0
Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service—0
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework—0
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data SelectionCode0
FarFetched: Entity-centric Reasoning and Claim Validation for the Greek Language based on Textually Represented EnvironmentsCode0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text RepresentationCode0
EconNLI: Evaluating Large Language Models on Economics ReasoningCode0
Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs—0
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion—0
"Seeing the Big through the Small": Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?Code0
ViANLI: Adversarial Natural Language Inference for Vietnamese—0
Exploring Factual Entailment with NLI: A News Media Study—0
Co-training for Low Resource Scientific Natural Language InferenceCode0
Hyperbolic sentence representations for solving Textual Entailment—0
FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in Biomedical DomainCode0
Post-Hoc Answer Attribution for Grounded and Trustworthy Long Document Comprehension: Task, Insights, and Challenges—0
Paraphrasing in Affirmative Terms Improves Negation Understanding—0
Sexism Detection on a Data Diet—0
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study—0
Do Language Models Understand Morality? Towards a Robust Detection of Moral ContentCode0
CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMsCode0
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models—0
An Analysis under a Unified Fomulation of Learning Algorithms with Output Constraints—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
Accurate and Nuanced Open-QA Evaluation Through Textual EntailmentCode0
New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in FrenchCode0
Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation—0
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference—0
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference CorpusCode0
Designing NLP Systems That Adapt to Diverse Worldviews—0
From Text to Context: An Entailment Approach for News Stakeholder ClassificationCode0
Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness—0
Detecting Statements in Text: A Domain-Agnostic Few-Shot SolutionCode0
The Effect of Model Size on LLM Post-hoc Explainability via LIMECode0
D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities of Large Language ModelsCode0
Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive StudyCode0
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach—0
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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