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 51–100 of 1961 papers

TitleStatusHype
Language Fusion for Parameter-Efficient Cross-lingual TransferCode0
Assessing Language Comprehension in Large Language Models Using Construction Grammar—0
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence BenchmarksCode0
From Superficial Patterns to Semantic Understanding: Fine-Tuning Language Models on Contrast Sets—0
HindiLLM: Large Language Model for Hindi—0
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference—0
Explainable Procedural Mistake Detection—0
On Adversarial Robustness and Out-of-Distribution Robustness of Large Language ModelsCode0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI—0
Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing—0
Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks—0
Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models—0
Transformer-Based Contextualized Language Models Joint with Neural Networks for Natural Language Inference in Vietnamese—0
PatentEdits: Framing Patent Novelty as Textual Entailment—0
Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical Concern-related App Reviews—0
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation—0
Explaining Mixtures of Sources in News Articles—0
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output—0
Teaching a Language Model to Distinguish Between Similar Details using a Small Adversarial Training Set—0
Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and ResolutionCode0
Natural Language Inference Improves Compositionality in Vision-Language Models—0
Flexible Natural Language-Based Image Data Downlink Prioritization for NanosatellitesCode0
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models—0
uOttawa at LegalLens-2024: Transformer-based Classification ExperimentsCode0
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine—0
Augmenting Legal Decision Support Systems with LLM-based NLI for Analyzing Social Media EvidenceCode0
SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning—0
From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items—0
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social MediaCode0
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation—0
VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language InferenceCode0
Inference and Verbalization Functions During In-Context LearningCode0
ALVIN: Active Learning Via INterpolation—0
Measuring the Groundedness of Legal Question-Answering Systems—0
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference—0
PublicHearingBR: A Brazilian Portuguese Dataset of Public Hearing Transcripts for Summarization of Long Documents—0
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMsCode0
Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation GenerationCode0
Reasoning with Natural Language Explanations—0
ECon: On the Detection and Resolution of Evidence ConflictsCode0
How Hard is this Test Set? NLI Characterization by Exploiting Training DynamicsCode0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
NL-Eye: Abductive NLI for Images—0
Multimodal Coherent Explanation Generation of Robot FailuresCode0
How Entangled is Factuality and Deception in German?—0
Assessment and manipulation of latent constructs in pre-trained language models using psychometric scalesCode0
Evaluation of OpenAI o1: Opportunities and Challenges of AGI—0
GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph KnowledgeCode1
Show:102550
← PrevPage 2 of 40Next →
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