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 201–250 of 1961 papers

TitleStatusHype
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator—0
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language ModelsCode0
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at ScaleCode2
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
FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim ExtractionCode1
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection—0
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models—0
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task AdaptationCode4
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information ExtractionCode0
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation TasksCode0
Citation-Enhanced Generation for LLM-based ChatbotsCode1
How Do Humans Write Code? Large Models Do It the Same Way TooCode0
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?Code0
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot ExamplesCode0
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic—0
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy—0
A synthetic data approach for domain generalization of NLI models—0
Comparing Hallucination Detection Metrics for Multilingual Generation—0
Strong hallucinations from negation and how to fix them—0
Conformalized Credal Set PredictorsCode0
LAPDoc: Layout-Aware Prompting for Documents—0
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion—0
Plausible Extractive Rationalization through Semi-Supervised Entailment SignalCode0
Pixel Sentence Representation LearningCode1
FaBERT: Pre-training BERT on Persian Blogs—0
A Hypothesis-Driven Framework for the Analysis of Self-Rationalising ModelsCode0
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on TextCode0
Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic RefinementCode0
MT-Ranker: Reference-free machine translation evaluation by inter-system rankingCode1
InfoLossQA: Characterizing and Recovering Information Loss in Text SimplificationCode0
Textual Entailment for Effective Triple Validation in Object PredictionCode0
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI ModelsCode0
Seed-Guided Fine-Grained Entity Typing in Science and Engineering DomainsCode0
Are self-explanations from Large Language Models faithful?Code1
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain—0
Model Editing Harms General Abilities of Large Language Models: Regularization to the RescueCode1
On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine LearningCode0
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing—0
Building Efficient Universal Classifiers with Natural Language InferenceCode1
L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMsCode0
Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs—0
Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs—0
Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free ApproachCode0
Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language InferenceCode0
Quantifying Divergence for Human-AI Collaboration and Cognitive TrustCode0
Large language models in healthcare and medical domain: A review—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