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 551–600 of 1961 papers

TitleStatusHype
Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules—0
Explaining Interactions Between Text SpansCode0
Ecologically Valid Explanations for Label Variation in NLICode0
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking—0
Investigating semantic subspaces of Transformer sentence embeddings through linear structural probingCode0
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation LearningCode0
Experimenting AI Technologies for Disinformation Combat: the IDMO Project—0
Calibrating Likelihoods towards Consistency in Summarization Models—0
Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization—0
A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores—0
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text ClassificationCode0
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP—0
Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All LabelsCode0
SplitEE: Early Exit in Deep Neural Networks with Split ComputingCode0
X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across ParagraphsCode0
Rethinking STS and NLI in Large Language Models—0
Self-Consistent Narrative Prompts on Abductive Natural Language InferenceCode0
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models—0
OYXOY: A Modern NLP Test Suite for Modern GreekCode0
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task—0
EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
A deep Natural Language Inference predictor without language-specific training data—0
Exploiting Language Models as a Source of Knowledge for Cognitive Agents—0
BatchPrompt: Accomplish more with lessCode0
Link Prediction for Wikipedia Articles as a Natural Language Inference TaskCode0
Lightweight Adaptation of Neural Language Models via Subspace EmbeddingCode0
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation ClassificationCode0
Towards Controllable Natural Language Inference through Lexical Inference Types—0
Improving Domain-Specific Retrieval by NLI Fine-Tuning—0
An Overview Of Temporal Commonsense Reasoning and Acquisition—0
ARC-NLP at PAN 2023: Transition-Focused Natural Language Inference for Writing Style Detection—0
Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-TrainingCode0
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language UnderstandingCode0
Improving Zero-shot Relation Classification via Automatically-acquired Entailment Templates—0
Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language InferenceCode0
LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention BiasCode0
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic—0
SpaceNLI: Evaluating the Consistency of Predicting Inferences in SpaceCode0
Evaluating Paraphrastic Robustness in Textual Entailment Models—0
Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language ModelsCode0
No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference—0
Pushing the Limits of ChatGPT on NLP Tasks—0
Neural models for Factual Inconsistency Classification with ExplanationsCode0
FLamE: Few-shot Learning from Natural Language Explanations—0
NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processing—0
Analysis of the Fed's communication by using textual entailment model of Zero-Shot classification—0
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts—0
Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairsCode0
Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UnitedSynT5 (3B)% Test Accuracy94.7—Unverified
2UnitedSynT5 (335M)% Test Accuracy93.5—Unverified
3EFL (Entailment as Few-shot Learner) + RoBERTa-large% Test Accuracy93.1—Unverified
4Neural Tree Indexers for Text Understanding% 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 11BAccuracy92.5—Unverified
9T5-XXL 11B (fine-tuned)Accuracy92.5—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
8DeBERTa (large)Matched91.1—Unverified
9Adv-RoBERTa ensembleMatched91.1—Unverified
10SMARTRoBERTaDev Matched91.1—Unverified