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Explanation Generation

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평가표

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보존된 평가 기록

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코드

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

https://github.com/jeromeramos70/peapod

공식 코드

Preference Distillation for Personalized Generative Recommendation

https://github.com/jiachengli1995/ucepic

공식 코드

UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation

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

공식 코드

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

https://github.com/jiuzhouh/reward-engineering-for-generating-seg

공식 코드

Reward Engineering for Generating Semi-structured Explanation

https://github.com/jiwanchung/vlis

공식 코드

VLIS: Unimodal Language Models Guide Multimodal Language Generation

https://github.com/jvladika/healthfc

공식 코드

HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking

https://github.com/kalendsyang/realfactbench

공식 코드

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking

https://github.com/kominaru/brie

공식 코드

Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability

https://github.com/ku-milab/lear

공식 코드

Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model

https://github.com/linguisticanomalies/pls_retrieval

공식 코드

Retrieval augmentation of large language models for lay language generation

https://github.com/linkedin/TE2Rules

공식 코드

TE2Rules: Explaining Tree Ensembles using Rules

https://github.com/liqiangjing/team

공식 코드

Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation

https://github.com/lzw108/fmd

공식 코드

FMDLlama: Financial Misinformation Detection based on Large Language Models

https://github.com/mainlp/litex

공식 코드

LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference

https://github.com/mainuliitkgp/ar-bert

공식 코드

AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations

https://github.com/manga-uofa/epr

공식 코드

Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic

https://github.com/mdda/worldtree_corpus

공식 코드

Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation

https://github.com/mdmustafizurrahman/An-Information-Retrieval-Approach-to-Building-Datasets-for-Hate-Speech-Detection

공식 코드

An Information Retrieval Approach to Building Datasets for Hate Speech Detection

https://github.com/microsoft/automated-explanations

공식 코드

Explaining black box text modules in natural language with language models

https://github.com/microsoft/recai

공식 코드

RecExplainer: Aligning Large Language Models for Explaining Recommendation Models

https://github.com/mismatchquest/mismatchquest

공식 코드

Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment

https://github.com/mumen798/pc-crs

공식 코드

Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations

https://github.com/neemakot/Health-Fact-Checking

공식 코드

Explainable Automated Fact-Checking for Public Health Claims

https://github.com/nehasrikn/elaborative-simplification

공식 코드

Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

https://github.com/neuhai/explanation-enriched-active-learning

공식 코드

Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture

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

공식 코드

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

https://github.com/orcax/LOGER

공식 코드

Faithfully Explainable Recommendation via Neural Logic Reasoning

https://github.com/ouyangkun10/edge

공식 코드

Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue

https://github.com/peterbhase/LAS-NL-Explanations

공식 코드

Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?

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

공식 코드

Multimodal Coherent Explanation Generation of Robot Failures

https://github.com/qiaw99/Cross-Refine

공식 코드

Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem

https://github.com/qtli/eib

공식 코드

Explanation Regeneration via Information Bottleneck

https://github.com/rashidrao-pk/lime-stratified-examples

공식 코드

Using Stratified Sampling to Improve LIME Image Explanations

https://github.com/rashidrao-pk/lime_stratified

공식 코드

Using Stratified Sampling to Improve LIME Image Explanations

https://github.com/ruc-wjyu/iot-match

공식 코드

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

https://github.com/rucaibox/ievalm-crs

공식 코드

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

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