% Key papers for In-context learning analysis (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2021_true_62274,
  title = {{True Few-Shot Learning with Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28553},
}

@inproceedings{neurips2022_generating_63859,
  title = {{Generating Training Data with Language Models: Towards Zero-Shot Language Understanding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52982},
}

@inproceedings{iclr2022_differentiable_31519,
  title = {{Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6259},
}

@inproceedings{icml2021_calibrate_39123,
  title = {{Calibrate Before Use: Improving Few-shot Performance of Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10185},
}

@inproceedings{iclr2023_selective_33223,
  title = {{Selective Annotation Makes Language Models Better Few-Shot Learners}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11980},
}

@inproceedings{aaai2023_adept_3038,
  title = {{ADEPT: A DEbiasing PrompT Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26279},
}

@inproceedings{neurips2023_counterfactual_66870,
  title = {{Counterfactual Memorization in Neural Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72772},
}

@inproceedings{neurips2023_large_67220,
  title = {{Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72494},
}

@inproceedings{icml2023_compositional_41736,
  title = {{Compositional Exemplars for In-context Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24017},
}

@inproceedings{neurips2023_fairness_67334,
  title = {{Fairness-guided Few-shot Prompting for Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72392},
}

@inproceedings{iclr2022_an_31280,
  title = {{An Explanation of In-context Learning as Implicit Bayesian Inference}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6893},
}

@inproceedings{aaai2025_causal_8398,
  title = {{Causal Prompting: Debiasing Large Language Model Prompting Based on Front-Door Adjustment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34777},
}

@inproceedings{iclr2023_bidirectional_33360,
  title = {{Bidirectional Language Models Are Also Few-shot Learners}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11141},
}

@inproceedings{aaai2022_cins_1812,
  title = {{CINS: Comprehensive Instruction for Few-Shot Learning in Task-Oriented Dialog Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/11076-cins-comprehensive-instruction-for-few-shot-learning-in-task-oriented-dialog-systems/},
}

@inproceedings{aaai2024_consistner_5473,
  title = {{ConsistNER: Towards Instructive NER Demonstrations for LLMs with the Consistency of Ontology and Context}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29892},
}

@inproceedings{iclr2023_k_32942,
  title = {{\$k\$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11117},
}

@inproceedings{neurips2023_the_68941,
  title = {{The Learnability of In-Context Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70923},
}

@inproceedings{aaai2024_adaptive_5134,
  title = {{Adaptive Prompt Routing for Arbitrary Text Style Transfer with Pre-trained Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29832},
}

@inproceedings{iclr2024_batch_34200,
  title = {{Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18875},
}

@inproceedings{cvpr2024_link_24013,
  title = {{Link-Context Learning for Multimodal LLMs}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31464},
}

@inproceedings{cvpr2025_the_27857,
  title = {{The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33481},
}

@inproceedings{aaai2024_dialogue_5685,
  title = {{Dialogue for Prompting: A Policy-Gradient-Based Discrete Prompt Generation for Few-Shot Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29809},
}

@inproceedings{iclr2024_muffin_33512,
  title = {{MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction Following}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19563},
}

@inproceedings{iclr2024_in_34651,
  title = {{In-Context Learning Learns Label Relationships but Is Not Conventional Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18423},
}

@inproceedings{icml2025_the_47828,
  title = {{The Surprising Effectiveness of Test-Time Training for Few-Shot Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44773},
}

@inproceedings{aaai2025_divide_8784,
  title = {{Divide-Solve-Combine: An Interpretable and Accurate Prompting Framework for Zero-shot Multi-Intent Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34688},
}

@inproceedings{iclr2024_in_34214,
  title = {{In-Context Pretraining: Language Modeling Beyond Document Boundaries}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18861},
}

@inproceedings{icml2024_in_44314,
  title = {{In-Context Principle Learning from Mistakes}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34138},
}

@inproceedings{neurips2024_probing_71290,
  title = {{Probing the Decision Boundaries of In-context Learning in Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95957},
}

@inproceedings{neurips2024_teach_71503,
  title = {{Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95758},
}

@inproceedings{iclr2025_promptriever_83471,
  title = {{Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28345},
}

@inproceedings{iclr2025_making_83992,
  title = {{Making Text Embedders Few-Shot Learners}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27833},
}

@inproceedings{aaai2025_guidener_9376,
  title = {{GuideNER: Annotation Guidelines Are Better than Examples for In-Context Named Entity Recognition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34591},
}

@inproceedings{iclr2024_unveiling_34719,
  title = {{Unveiling and Manipulating Prompt Influence in Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18355},
}

@inproceedings{neurips2024_on_70115,
  title = {{On the Noise Robustness of In-Context Learning for Text Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96962},
}