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Explanation Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IDT-ITI/T-TAME
공식 코드T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
https://github.com/JamesHujy/ELV
공식 코드Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
https://github.com/JoshuaGhost/expred
공식 코드https://github.com/LCS2-IIITD/TOXBART
공식 코드Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech
https://github.com/UKPLab/tacl2025-ood-eval-self-rationalization
공식 코드Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks
https://github.com/VT-NLP/Mocheg
공식 코드End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
https://github.com/Zarharan/NLE-for-fact-checking
공식 코드Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models
https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
공식 코드https://github.com/amazon-research/expunations
공식 코드https://github.com/analytics-everywhere-lab/vqixai
공식 코드https://github.com/anirbansarkar-cs/Ante-hoc_Explainability_Concepts
공식 코드A Framework for Learning Ante-hoc Explainable Models via Concepts
https://github.com/arobrecht/severus-study
공식 코드SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation Generation
https://github.com/bardhprenkaj/hansel
공식 코드Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes
https://github.com/bhaavya/instructgpt-analogies
공식 코드Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
https://github.com/chelsea234/m2f2_det
공식 코드Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
https://github.com/chen-zichen/xplainllm_dataset
공식 코드XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs
https://github.com/cognitive-robots/causal-natural-language-explanations
공식 코드https://github.com/collinskatie/structured_flexible_and_robust
공식 코드https://github.com/copenlu/spanex
공식 코드https://github.com/csinva/imodelsX
공식 코드Explaining Patterns in Data with Language Models via Interpretable Autoprompting
https://github.com/demoleiwang/llm4vis
공식 코드LLM4Vis: Explainable Visualization Recommendation using ChatGPT
https://github.com/dependentsign/EX-FEVER
공식 코드EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
https://github.com/dongmo-qcq/fg-cam
공식 코드https://github.com/dqwang122/rlhex
공식 코드Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
https://github.com/explainableml/clevr-x
공식 코드CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
https://github.com/firatcekinel/multi-task-fact-checking
공식 코드Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach
https://github.com/flamenlp/TURBO
공식 코드Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation
https://github.com/funket/zorro
공식 코드Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
https://github.com/google-research-datasets/QED
공식 코드QED: A Framework and Dataset for Explanations in Question Answering
https://github.com/groshanlal/nn2rules
공식 코드https://github.com/groshanlal/te2rules
공식 코드https://github.com/heidelberg-nlp/cc-shap-vlm
공식 코드https://github.com/hitsz-tmg/cognitive-visual-language-mapper
공식 코드A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
https://github.com/hoyuns/acl25-selective-reasoning-distillation
공식 코드https://github.com/iit-dm/fin-fact
공식 코드Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation
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