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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/neukg/robustmrc

공식 코드

An Understanding-Oriented Robust Machine Reading Comprehension Model

https://github.com/neuro-symbolic-ai/explanation_based_ethical_reasoning

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Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement

https://github.com/neuro-symbolic-ai/explanation_refinement

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Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving

https://github.com/neurocomplab-psu/eqprop-seqlearning

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Sequence Learning Using Equilibrium Propagation

https://github.com/ngregoriade/semeval2024-shroom

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AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis

https://github.com/nighttime/entgraph

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Smoothing Entailment Graphs with Language Models

https://github.com/nimameghdadi/uottawa-at-legallens-2024-transformer-based-classification

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uOttawa at LegalLens-2024: Transformer-based Classification Experiments

https://github.com/niru-umass-dev/caspr

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CASPR: Automated Evaluation Metric for Contrastive Summarization

https://github.com/njjiang/livenli

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Ecologically Valid Explanations for Label Variation in NLI

https://github.com/njjiang/nli_disagreement_taxonomy

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Investigating Reasons for Disagreement in Natural Language Inference

https://github.com/nlp-titech/headline-entailment

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Improving Truthfulness of Headline Generation

https://github.com/nlpaueb/greek-bert

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GREEK-BERT: The Greeks visiting Sesame Street

https://github.com/nlpie-research/lightweight-clinical-transformers

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Lightweight Transformers for Clinical Natural Language Processing

https://github.com/nouhadziri/DialogEntailment

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Evaluating Coherence in Dialogue Systems using Entailment

https://github.com/nrjvarshney/unsupervised_nli

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Unsupervised Natural Language Inference Using PHL Triplet Generation

https://github.com/ntunlp/chatgpt_eval

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A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets

https://github.com/ntunlplab/zara

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ZARA: Improving Few-Shot Self-Rationalization for Small Language Models

https://github.com/nyu-dl/dl4mt-seqgen

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A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models

https://github.com/nyu-mll/CNLI-generalization

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Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data

https://github.com/nyu-mll/semi-automatic-nli

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Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options

https://github.com/observeai-research/nli-fsl

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Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection

https://github.com/ofa-sys/ofa

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OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/ofa-x/ofa-x

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Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations

https://github.com/open-compass/anah

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ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models

https://github.com/open-social-world/egonormia

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EgoNormia: Benchmarking Physical Social Norm Understanding

https://github.com/openai/gpt-3

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Language Models are Few-Shot Learners

https://github.com/openmicg/mcg

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Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering

https://github.com/oreozhao/tea

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From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment

https://github.com/oriyor/ret-robust

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Making Retrieval-Augmented Language Models Robust to Irrelevant Context

https://github.com/osainz59/Ask2Transformers

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Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction

https://github.com/osu-nlp-group/attrscore

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Automatic Evaluation of Attribution by Large Language Models

https://github.com/owenzx/InstabilityAnalysis

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The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions

https://github.com/owenzx/LexicalDebias-ACL2020

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Towards Robustifying NLI Models Against Lexical Dataset Biases

https://github.com/oyar99/ir-ag-reg

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A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts

https://github.com/pablomessina/cxr-fact-encoder

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Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation

https://github.com/panatchakorn-a/bias-eval-nli-considering-all-labels

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Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels

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