Awesome-Explanation-Assisted-Learning

This repo is a paper collection of explanation assisted learning topic(explanation guided learning).

Survey

• [2024] [ACM] Gao, Yuyang, et al. Going beyond xai: A systematic survey for explanation-guided learning. [paper]

ICML/NeurIPS/ICLR Conference

• [2026] [ICML] Dawood Wasif, et al. Explainable Federated Learning via Global–Local Attribution Alignment. [paper]
• [2026] [ICML] Michal Moshkovitz, et al. Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods. [paper]
• [2026] [ICML] Doojin Baek, et al. Learning to Theorize the World from Observation. [paper]
• [2026] [ICLR] Shicheng Liu, Minghui Zhu. Leveraging Explanation to Improve Generalization of Meta Reinforcement Learning. [paper]
• [2026] [ICLR] Zhen Tan, et al. Probing to Refine: Reinforcement Distillation of LLM Reasoners via Explanatory Inversion. [paper]
• [2025] [NIPS] Alessandro Marchese, et al. Causal Explanation-Guided Learning for Organ Allocation. [paper]
• [2025] [NIPS] Awni Altabaa, et al. CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision. [paper]
• [2025] [NIPS] Jusheng Zhang, et al. CF-VLM: CounterFactual Vision-Language Fine-tuning. [paper]
• [2025] [NIPS] Shufan Shen, et al. VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set. [paper]
• [2025] [NIPS] Faisal Hamman, et al. Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations. [paper]
• [2025] [NIPS] Keshav Ramji, et al. Latent Principle Discovery for Language Model Self-Improvement. [paper]
• [2025] [NIPS] Anthony Fuller, et al. LookWhere? Efficient Visual Recognition by Learning Where to Look and What to See from Self-Supervision[paper]
• [2025] [NIPS] Ruiyang Zhou, et al. ExPO: Unlocking Hard Reasoning with Self-Explanation-Guided Reinforcement Learning[paper]
• [2025] [NIPS] Jongseo Lee, et al. Disentangled Concepts Speak Louder Than Words: Explainable Video Action Recognition[paper]
• [2025] [ICML] Osman Berke Guney, et al. Active feature acquisition via explainability-driven ranking[paper]
• [2025] [ICLR] Junqi Jiang, et al. Interpreting Language Reward Models via Contrastive Explanations[paper]
• [2025] [ICLR] Hanning Guo, et al. XAIguiFormer: explainable artificial intelligence guided transformer for brain disorder identification. [paper]
• [2025] [ICLR] Lirong Wu, et al. A Simple yet Effective $\Delta \Delta G$ Predictor is An Unsupervised Antibody Optimizer and Explainer. [paper]
• [2025] [ICLR] Shicheng Liu, et al. UTILITY: Utilizing Explainable Reinforcement Learning to Improve Reinforcement Learning. [paper]
• [2024] [NIPS] Jiang Nan, et al. LeDex: Training LLMs to Better Self-Debug and Explain Code.[paper]
• [2024] [NIPS] Yuefei Lyu, et al. Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning. paper
• [2024] [NIPS] Yingjun Du, et al. IPO: Interpretable Prompt Optimization for Vision-Language Models. paper
• [2024] [NIPS] Farnoush Rezaei Jafari, et al. MambaLRP: Explaining Selective State Space Sequence Models. paper
• [2024] [NIPS] Rohan Paleja, et al. Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems. paper
• [2024] [NIPS] Jason Gross, et al. Compact Proofs of Model Performance via Mechanistic Interpretability. paper
• [2024] [NIPS] Pasan Dissanayake, Sanghamitra Dutta. Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory. paper
• [2024] [ICML] Christian Bjørn, et al. Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks. [paper]
• [2024] [ICML] Zelei Cheng, et al. RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation.[paper] [code]
• [2024] [ICML] Zheng Huang et al. Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning. [paper] [code]
• [2024] [ICLR] Zijian Feng, et al. Unveiling and manipulating prompt influence in large language models. [paper] [code].
• [2024] [ICLR] Chongyu Fan, et al. SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. [paper] [code]
• [2024] [ICLR] Akari Asai, et al. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. [paper] [code]
• [2024] [ICLR] Anshuman Chhabra, et al. "What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection. [paper] [code]

Other Conference

• [2026] [FOCS] Steve Hanneke, et al. Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End. [paper]
• [2025] [COLT] Nirmit Joshi, et al. A Theory of Learning with Autoregressive Chain of Thought. [paper]

Journal

Other repos

• Awesome Explanatory Supervision [link]