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 501–550 of 1961 papers

TitleStatusHype
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?Code0
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
Conformalized Credal Set PredictorsCode0
Comparing Hallucination Detection Metrics for Multilingual Generation—0
Strong hallucinations from negation and how to fix them—0
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
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
Textual Entailment for Effective Triple Validation in Object PredictionCode0
InfoLossQA: Characterizing and Recovering Information Loss in Text SimplificationCode0
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI ModelsCode0
Seed-Guided Fine-Grained Entity Typing in Science and Engineering DomainsCode0
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain—0
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
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
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
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text GenerationCode0
Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs—0
Downstream Trade-offs of a Family of Text WatermarksCode0
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals—0
Human-in-the-loop: Towards Label Embeddings for Measuring Classification DifficultyCode0
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response GenerationCode0
Formal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inference—0
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided SearchCode0
Using Natural Language Explanations to Improve Robustness of In-context LearningCode0
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
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All datasetsSNLIRTEMultiNLIQNLIANLI testWNLILiDiRusRCBTERRaCommitmentBankSciTailFarsTail

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