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Natural Language Inference

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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:

Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/pauldrm/targeted_intervention

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HSI: Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models

https://github.com/pavel-blinov/RuMedBench

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RuMedBench: A Russian Medical Language Understanding Benchmark

https://github.com/pefimov/cross-lingual-adjustment

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The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer

https://github.com/persiannlp/parsinlu

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ParsiNLU: A Suite of Language Understanding Challenges for Persian

https://github.com/peteriz/academic-budget-bert

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How to Train BERT with an Academic Budget

https://github.com/phanchauthang/dsaa-2023-kaggle

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Link Prediction for Wikipedia Articles as a Natural Language Inference Task

https://github.com/phoenixsecularbird/r2f

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R$^2$F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference

https://github.com/pkhdipraja/in-context-atlas

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The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation

https://github.com/pluslabnlp/amrfact

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AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation

https://github.com/pnborchert/flare

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Language Fusion for Parameter-Efficient Cross-lingual Transfer

https://github.com/pradippramanick/coexp-iros24

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Multimodal Coherent Explanation Generation of Robot Failures

https://github.com/preke/affective-nli

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Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation

https://github.com/princeton-nlp/SimCSE

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SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/princeton-nlp/datamux

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DataMUX: Data Multiplexing for Neural Networks

https://github.com/princeton-nlp/mabel

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MABEL: Attenuating Gender Bias using Textual Entailment Data

https://github.com/psunlpgroup/ConEntail

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ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining

https://github.com/pujari-rajkumar/naacl2019

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Using Natural Language Relations between Answer Choices for Machine Comprehension

https://github.com/puredefence/advbench4text

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Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks

https://github.com/pytorch/fairseq

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/pytorch/fairseq/tree/main/examples/data2vec

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data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

https://github.com/pytorch/fairseq/tree/main/examples/xglm

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Few-shot Learning with Multilingual Language Models

https://github.com/pytorch/fairseq/tree/master/examples/rxf

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Better Fine-Tuning by Reducing Representational Collapse

https://github.com/rabeehk/robust-nli

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End-to-End Bias Mitigation by Modelling Biases in Corpora

https://github.com/rabeehk/robust-nli-fixed

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End-to-End Bias Mitigation by Modelling Biases in Corpora

https://github.com/rabeehk/vibert

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Variational Information Bottleneck for Effective Low-Resource Fine-Tuning

https://github.com/rachneet/cross-task-figurative-explanations

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Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5

https://github.com/raunak-agarwal/factual-consistency-eval

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Zero-shot Factual Consistency Evaluation Across Domains

https://github.com/regnlp/obligationclassifier

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RIRAG: Regulatory Information Retrieval and Answer Generation

https://github.com/regnlp/obliqadataset

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RIRAG: Regulatory Information Retrieval and Answer Generation

https://github.com/reml-group/dog

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Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models

https://github.com/researchmm/soho

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Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

https://github.com/responsibleailab/halo

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HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making

https://github.com/rmit-ir/GRAG-LiveRAG

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RMIT-ADM+S at the SIGIR 2025 LiveRAG Challenge

https://github.com/rohitrai11/application-specific-compression-of-deep-learning-models

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Application Specific Compression of Deep Learning Models

https://github.com/ruc-gsai/yulan-ir

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RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit

https://github.com/ruiqi-zhong/Meta-tuning

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Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.