負の影響「AIが誤情報を出力」

RAGへのポイズニング攻撃

• Poisoning Retrieval Corpora by Injecting Adversarial Passages, 2023
• BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models, 2024
• PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models, 2024
• Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications, 2024
• Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems, 2025

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ハルシネーション

• The Reversal Curse: LLMs trained on “A is B” fail to learn “B is A”, 2023
• Why Does ChatGPT Fall Short in Providing Truthful Answers?, 2023
• DefAn: Definitive-Answer-Dataset-for-LLMs-Hallucination-Evaluation, 2024
• LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples, 2024
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models, 2024

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データキュレーション

• Datasheets for Datasets, 2021.0
• Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks, 2021.0
• Confident Learning: Estimating Uncertainty in Dataset Labels, 2022.0

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RAG

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020.0
• REALM: Retrieval-Augmented Language Model Pre-Training, 2020.0
• In-Context Retrieval-Augmented Language Models, 2023.0
• Active Retrieval Augmented Generation, 2023.0
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, 2023.0
• Query Rewriting for Retrieval-Augmented Large Language Models, 2023.0
• Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, 2023.0
• Generate rather than Retrieve: Large Language Models are Strong Context Generators, 2023.0
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy, 2023.0
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization, 2024.0

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XAI（説明可能なAI）

• Visualizing and Understanding Convolutional Networks, 2014.0
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014.0
• Understanding Deep Image Representations by Inverting Them, 2014.0
• “Why Should I Trust You?”: Explaining the Predictions of Any Classifier, 2016.0
• A Unified Approach to Interpreting Model Predictions, 2017.0
• Learning Important Features Through Propagating Activation Differences, 2017.0
• Understanding Black-box Predictions via Influence Functions, 2017.0
• Interpretable Explanations of Black Boxes by Meaningful Perturbation, 2017.0
• Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018.0
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, 2019.0

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ハルシネーションの検知

• Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis, 2023.0
• Cost-Effective Hallucination Detection for LLMs, 2024.0
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models, 2024.0
• Measuring and Reducing LLM Hallucination without Gold-Standard Answers, 2024.0
• On Large Language Models’ Hallucination with Regard to Known Facts, 2024.0

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不確実性の定量化

• Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding, 2015.0
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, 2016.0
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, 2017.0
• Predictive Uncertainty Estimation via Prior Networks, 2018.0
• Evidential Deep Learning to Quantify Classification Uncertainty, 2018.0
• Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift, 2019.0
• Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods, 2021.0

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教育やフォローアップ

• What Students Can Learn About Artificial Intelligence — Recommendations for K-12 Computing Education, 2022.0
• Learning to Prompt in the Classroom to Understand AI Limits: A pilot study, 2023.0
• Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study, 2024.0
• The Essentials of AI for Life and Society: An AI Literacy Course for the University Community, 2025.0