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 151–200 of 1961 papers

TitleStatusHype
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
Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playingCode1
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
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach—0
Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem ProvingCode0
Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social MediaCode0
DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training—0
FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking—0
Don't Say No: Jailbreaking LLM by Suppressing RefusalCode1
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks—0
CASPR: Automated Evaluation Metric for Contrastive SummarizationCode0
Marking: Visual Grading with Highlighting Errors and Annotating Missing BitsCode0
Automated Long Answer Grading with RiceChem DatasetCode0
Explanation based Bias Decoupling Regularization for Natural Language Inference—0
How often are errors in natural language reasoning due to paraphrastic variability?—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report AnalysisCode0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
XNLIeu: a dataset for cross-lingual NLI in BasqueCode0
SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials—0
A Morphology-Based Investigation of Positional Encodings—0
IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical TrialsCode0
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered StudyCode0
Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to LuxembourgishCode0
SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language Models on Natural Language Inference for Clinical Trials—0
Evaluating Generative Language Models in Information Extraction as Subjective Question CorrectionCode0
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in ConversationCode0
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference—0
On the Role of Summary Content Units in Text Summarization EvaluationCode0
Evaluating Large Language Models Using Contrast Sets: An Experimental Approach—0
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches—0
AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysisCode0
Unveiling Divergent Inductive Biases of LLMs on Temporal DataCode0
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4Code0
Adverb Is the Key: Simple Text Data Augmentation with Adverb DeletionCode0
FACTOID: FACtual enTailment fOr hallucInation Detection—0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications—0
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs—0
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis—0
Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology—0
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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