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 451–500 of 1961 papers

TitleStatusHype
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training—0
Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social MediaCode0
FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking—0
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
Language Models Meet Anomaly Detection for Better Interpretability and GeneralizabilityCode0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
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
SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language Models on Natural Language Inference for Clinical Trials—0
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
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
Unveiling Divergent Inductive Biases of LLMs on Temporal DataCode0
AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysisCode0
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
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs—0
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications—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
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator—0
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language ModelsCode0
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
Exploring Continual Learning of Compositional Generalization in NLICode0
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition—0
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection—0
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information ExtractionCode0
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models—0
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation TasksCode0
How Do Humans Write Code? Large Models Do It the Same Way TooCode0
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot ExamplesCode0
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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