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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/utahnlp/knowledge_infotabs

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Incorporating External Knowledge to Enhance Tabular Reasoning

https://github.com/utahnlp/layer_augmentation

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Augmenting Neural Networks with First-order Logic

https://github.com/utahnlp/tabpert

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TabPert: An Effective Platform for Tabular Perturbation

https://github.com/uva-slpl/embedalign

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Deep Generative Model for Joint Alignment and Word Representation

https://github.com/vanh17/textsim

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How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks

https://github.com/vectara/mirage-bench

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MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems

https://github.com/vene/sparsemap

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SparseMAP: Differentiable Sparse Structured Inference

https://github.com/venelink/inferes

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InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples

https://github.com/veronica320/recursive-nps

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Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases

https://github.com/verypluming/HELP

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HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning

https://github.com/verypluming/MED

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Can neural networks understand monotonicity reasoning?

https://github.com/verypluming/jsick

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Compositional Evaluation on Japanese Textual Entailment and Similarity

https://github.com/verypluming/transitivity

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Exploring Transitivity in Neural NLI Models through Veridicality

https://github.com/vid-koci/bert-commonsense

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A Surprisingly Robust Trick for Winograd Schema Challenge

https://github.com/vijit-m/TablEval

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BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with Tables

https://github.com/vipulgupta1011/calm

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CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias

https://github.com/virginie-do/e-SNLI-VE

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e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations

https://github.com/visual-text-QA/VTQA-Demo

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VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning

https://github.com/wangskygit/passage-sieve

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Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization

https://github.com/wangzc1233/Prot2Chat

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Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure

https://github.com/wbrun0/LawngNLI

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LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrieval

https://github.com/wendywangwwt/zero-shot-complex-question-answering-on-long-scientific-documents

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Zero-Shot Complex Question-Answering on Long Scientific Documents

https://github.com/wentaotan/sena

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Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution

https://github.com/wgantt/nli-mixed-models

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Natural Language Inference with Mixed Effects

https://github.com/wj210/NLI_ETP

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Plausible Extractive Rationalization through Semi-Supervised Entailment Signal

https://github.com/wjw136/clean_lave

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On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction

https://github.com/wlzhang2020/reasonrag

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Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning

https://github.com/wuhy68/p-adapter

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p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models

https://github.com/xfactlab/emnlp2023-llm-disagreement

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Can Large Language Models Capture Dissenting Human Voices?

https://github.com/xhan77/influence-function-analysis

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Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

https://github.com/xhz0809/openstance

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OpenStance: Real-world Zero-shot Stance Detection

https://github.com/xiye17/explselection

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Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting

https://github.com/xiye17/interpcalib

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Can Explanations Be Useful for Calibrating Black Box Models?

https://github.com/xiye17/textualexplincontext

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The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning

https://github.com/xlhex/acl2024_xicl

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Using Natural Language Explanations to Improve Robustness of In-context Learning

https://github.com/xwgeng/SSAN

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How Does Selective Mechanism Improve Self-Attention Networks?

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