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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/jihaonew/mm-instruct

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MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment

https://github.com/jimmycode/gen-debiased-nli

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Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets

https://github.com/jind11/TextFooler

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Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

https://github.com/jind11/utterance-rewriting

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Improving Bot Response Contradiction Detection via Utterance Rewriting

https://github.com/jingyng/label-adaptive-self-rationalization

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Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation

https://github.com/jinlanfu/polyglot_prompt

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Polyglot Prompt: Multilingual Multitask PrompTraining

https://github.com/jinyuanli0012/pgim

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LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition

https://github.com/jjy961228/ConCSE

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ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings

https://github.com/joeljang/elm

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Exploring the Benefits of Training Expert Language Models over Instruction Tuning

https://github.com/joestacey/NLI_with_a_human_touch

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Supervising Model Attention with Human Explanations for Robust Natural Language Inference

https://github.com/joestacey/atomic_inference_anli

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Atomic Inference for NLI with Generated Facts as Atoms

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

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Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training

https://github.com/joestacey/snli_logic

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Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models

https://github.com/joey234/nan-nli

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Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation

https://github.com/jordantab/llm-robustness-experiment

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On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models

https://github.com/joshbambrick/falsesum

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Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization

https://github.com/joykirat18/how-to-think-step-by-step

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How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning

https://github.com/jplalor/py-irt

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Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds

https://github.com/jstarc/nli_generation

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Constructing a Natural Language Inference Dataset using Generative Neural Networks

https://github.com/juand-r/x-parade

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X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs

https://github.com/juletx/self-translate

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Do Multilingual Language Models Think Better in English?

https://github.com/junyitao/infer-then-verbalize-during-icl

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Inference and Verbalization Functions During In-Context Learning

https://github.com/justinphan3110/SciFive

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SciFive: a text-to-text transformer model for biomedical literature

https://github.com/jvladika/nli4ct

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Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports

https://github.com/kaayy/structured-attention

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Textual Entailment with Structured Attentions and Composition

https://github.com/kai-wen-yang/qvix

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Good Questions Help Zero-Shot Image Reasoning

https://github.com/kaist-lklab/cot-collection

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The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaistai/cot-collection

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The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kakaobrain/KorNLUDatasets

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KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding

https://github.com/kali-hac/chatgpt-mbti

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Can ChatGPT Assess Human Personalities? A General Evaluation Framework

https://github.com/kanekomasahiro/discussion_nlp

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Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach

https://github.com/kanishkamisra/nafips2021

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Finding Fuzziness in Neural Network Models of Language Processing

https://github.com/katharinahech/labelembeddings

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Human-in-the-loop: Towards Label Embeddings for Measuring Classification Difficulty

https://github.com/keremzaman/explainli

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A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference

https://github.com/khloud-al/arnli

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ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection

https://github.com/khuangaf/chocolate

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Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning

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