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 351–400 of 1961 papers

TitleStatusHype
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Explainable Procedural Mistake Detection—0
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference—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
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation—0
Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical Concern-related App Reviews—0
Explaining Mixtures of Sources in News Articles—0
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output—0
Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and ResolutionCode0
Teaching a Language Model to Distinguish Between Similar Details using a Small Adversarial Training Set—0
Natural Language Inference Improves Compositionality in Vision-Language Models—0
uOttawa at LegalLens-2024: Transformer-based Classification ExperimentsCode0
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
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
From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items—0
SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning—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
Inference and Verbalization Functions During In-Context LearningCode0
VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language InferenceCode0
Measuring the Groundedness of Legal Question-Answering Systems—0
ALVIN: Active Learning Via INterpolation—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
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
How Hard is this Test Set? NLI Characterization by Exploiting Training DynamicsCode0
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
DisGeM: Distractor Generation for Multiple Choice Questions with Span MaskingCode0
AXCEL: Automated eXplainable Consistency Evaluation using LLMs—0
Using Similarity to Evaluate Factual Consistency in Summaries—0
Towards Building Efficient Sentence BERT Models using Layer Pruning—0
Enhancing SLM via ChatGPT and Dataset Augmentation—0
Application Specific Compression of Deep Learning ModelsCode0
A Comparative Study of Pre-training and Self-trainingCode0
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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