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 251–300 of 1961 papers

TitleStatusHype
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
The ICL Consistency Test—0
An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge GraphCode0
A Generic NLI approach for Classification of Sentiment Associated with Therapies—0
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples GenerationCode0
Downstream Trade-offs of a Family of Text WatermarksCode0
Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs—0
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text GenerationCode0
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals—0
Human-in-the-loop: Towards Label Embeddings for Measuring Classification DifficultyCode0
Formal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inference—0
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response GenerationCode0
Using Natural Language Explanations to Improve Robustness of In-context LearningCode0
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language ModelsCode1
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided SearchCode0
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument ExtractionCode1
Deep Natural Language Feature Learning for Interpretable PredictionCode0
Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation ModelsCode0
Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval—0
Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data—0
Not all layers are equally as important: Every Layer Counts BERT—0
SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes—0
Exploring the Boundaries of GPT-4 in Radiology—0
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical DomainCode2
Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules—0
Ecologically Valid Explanations for Label Variation in NLICode0
Explaining Interactions Between Text SpansCode0
Fast and Accurate Factual Inconsistency Detection Over Long DocumentsCode1
Investigating semantic subspaces of Transformer sentence embeddings through linear structural probingCode0
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking—0
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
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded HallucinationsCode1
A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores—0
Making Retrieval-Augmented Language Models Robust to Irrelevant ContextCode1
Augmenting Transformers with Recursively Composed Multi-grained RepresentationsCode1
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular QuantizersCode2
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
Rethinking STS and NLI in Large Language Models—0
X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across ParagraphsCode0
Self-Consistent Narrative Prompts on Abductive Natural Language InferenceCode0
OYXOY: A Modern NLP Test Suite for Modern GreekCode0
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models—0
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
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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