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 801–850 of 1961 papers

TitleStatusHype
Bridging Knowledge Gaps in Neural Entailment via Symbolic Models—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generating Contradictory, Neutral, and Entailing Sentences—0
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets—0
Conformal Predictor for Improving Zero-shot Text Classification Efficiency—0
Efficient Logical Inference for Semantic Processing—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
No Gold Standard, No Problem: Reference-Free Evaluation of Taxonomies—0
Annotating Relation Inference in Context via Question Answering—0
Improved Representation Learning for Question Answer Matching—0
Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks—0
Generating Token-Level Explanations for Natural Language Inference—0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
Efficiency in Ambiguity: Two Models of Probabilistic Semantics for Natural Language—0
Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading—0
A Hybrid System to apply Natural Language Inference over Dependency Trees—0
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
Giving AI Personalities Leads to More Human-Like Reasoning—0
GKR: the Graphical Knowledge Representation for semantic parsing—0
Global Inference for Bridging Anaphora Resolution—0
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study—0
Context-theoretic Semantics for Natural Language: an Algebraic Framework—0
BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables—0
GLUECoS : An Evaluation Benchmark for Code-Switched NLP—0
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting—0
Good, Great, Excellent: Global Inference of Semantic Intensities—0
Improved Sentence Modeling using Suffix Bidirectional LSTM—0
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation—0
Graph-Based Approach to Recognizing CST Relations in Polish Texts—0
Graph-based Retrieval for Claim Verification over Cross-Document Evidence—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework—0
IMPLI: Investigating NLI Models' Performance on Figurative Language—0
A Study of the Plausibility of Attention between RNN Encoders in Natural Language Inference—0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction—0
Grounded in Context: Retrieval-Based Method for Hallucination Detection—0
"I'm Not Mad": Commonsense Implications of Negation and Contradiction—0
Annotating Factive Verbs—0
Convolutional Interaction Network for Natural Language Inference—0
haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTE—0
Harmonization of conflicting medical opinions using argumentation protocols and textual entailment - a case study on Parkinson disease—0
HausaNLP at SemEval-2025 Task 3: Towards a Fine-Grained Model-Aware Hallucination Detection—0
Have Attention Heads in BERT Learned Constituency Grammar?—0
HDU: Cross-lingual Textual Entailment with SMT Features—0
``I'm Not Mad'': Commonsense Implications of Negation and Contradiction—0
IMPLI: Investigatng NLI Models' Performance on Figurative Language—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment—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