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https://openreview.net/forum?id=vLttpF8AOv In-Context Learning of Soft Nearest Neighbor Classifiers for Intelligible Tabular Machine Learning... With in-context learning foundation models like TabPFN excelling on small supervised tabular learning tasks, it has been argued that "boosted trees are not the... in context learning nearest neighbor classifiers https://research.google/blog/more-efficient-in-context-learning-with-glam/?ref=thefragilesea.com More Efficient In-Context Learning with GLaM Posted by Andrew M Dai and Nan Du, Research Scientists, Google Research, Brain Team Large language models (e.g., GPT-3) have many significant capab... in context learning efficient glam https://openreview.net/forum?id=G4NBKhLKYG Is In-Context Learning Sufficient for Instruction Following in LLMs? | OpenReview In-context learning (ICL) allows LLMs to learn from examples without changing their weights: this is a particularly promising capability for long-context LLMs... in context learning sufficient instruction following llms https://openreview.net/forum?id=p53QDxSIc5 DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction | OpenReview There is currently a significant gap between the performance of fine-tuned models and prompting approaches using Large Language Models (LLMs) on the... in context learning https://www.databricks.com/blog/llm-evaluation-for-icl Blazingly Fast LLM Evaluation for In-Context Learning | Databricks Blog With MosaicML you can now evaluate LLMs on in-context learning tasks (LAMBADA, HellaSwag, PIQA, and more) hundreds of times faster than other evaluation... in context learning llm evaluation fast databricks blog https://aclanthology.org/2023.findings-emnlp.12/ On the Relation between Sensitivity and Accuracy in In-Context Learning - ACL Anthology Yanda Chen, Chen Zhao, Zhou Yu, Kathleen McKeown, He He. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023. in context learning https://aclanthology.org/2023.findings-emnlp.675/ Exploring In-Context Learning for Knowledge Grounded Dialog Generation - ACL Anthology Qinyu Chen, Wenhao Wu, Sujian Li. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023. in context learning exploring https://aclanthology.org/2023.findings-emnlp.152/ Generative Calibration for In-context Learning - ACL Anthology Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023. in context learning generative calibration acl anthology https://openreview.net/forum?id=LrPSJTNfmt Enhancing Fairness in In-Context Learning: Prioritizing Minority Samples in Demonstrations |... Recent studies highlight the effectiveness of using in-context learning to steer large language models (LLMs) in processing tabular data, a challenging task... in context learning enhancing fairness prioritizing minority https://openreview.net/forum?id=W2CkzaqQnG GAMformer: Exploring In-Context Learning for Generalized Additive Models | OpenReview Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data.... in context learning generalized additive models exploring openreview https://aclanthology.org/2023.acl-long.783/ Mitigating Label Biases for In-context Learning - ACL Anthology Yu Fei, Yifan Hou, Zeming Chen, Antoine Bosselut. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long... in context learning mitigating label biases acl https://aclanthology.org/2025.findings-naacl.91/ Data Poisoning for In-context Learning - ACL Anthology Pengfei He, Han Xu, Yue Xing, Hui Liu, Makoto Yamada, Jiliang Tang. Findings of the Association for Computational Linguistics: NAACL 2025. 2025. in context learning data poisoning acl anthology https://openreview.net/forum?id=lfxIASyLxB In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness |... A striking property of transformers is their ability to perform in-context learning (ICL), a machine learning framework in which the learner is presented with... in context learning transformers https://openreview.net/forum?id=uILj5HPrag DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning | OpenReview In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task... in context learning detail task demonstration attribution https://aclanthology.org/2023.acl-long.256/ Unified Demonstration Retriever for In-Context Learning - ACL Anthology Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu. Proceedings of the 61st Annual Meeting of the Association... in context learning unified demonstration retriever acl https://openreview.net/forum?id=1DP5fR3iTr PFNs4BO: In-Context Learning for Bayesian Optimization | OpenReview In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to... in context learning bayesian optimization openreview https://openreview.net/forum?id=IjbXZdugdj&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2025%2FConference%2FAuthors%23your-submissions) Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical... Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine.... in context learning generative modeling https://www.amazon.science/publications/iclforge-enhancing-in-context-learning-with-evolutionary-algorithms-under-budgeted-annotation IclForge: Enhancing in-context learning with evolutionary algorithms under budgeted annotation -... In-context learning (ICL) has emerged as a powerful paradigm for adapting Large Language Models (LLMs) to specific tasks without parameter updates. While... in context learning evolutionary algorithms enhancing https://openreview.net/forum?id=WOa96EG26M Why Larger Language Models Do In-context Learning Differently? | OpenReview Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen... in context learning language models larger differently openreview https://openreview.net/forum?id=kExt2d4aLo Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective | OpenReview This work is about estimating when a conditional generative model (CGM) can solve an in-context learning (ICL) problem. An in-context learning (ICL) problem... in context learning https://openreview.net/forum?id=xpYqD7CRPW The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains | OpenReview Large language models have the ability to generate text that mimics patterns in their inputs. We introduce a simple Markov Chain (MC) sequence modeling task in... in context learning the evolution statistical induction https://research.google/pubs/batch-calibration-rethinking-calibration-for-in-context-learning-and-prompt-engineering/ Batch Calibration: Rethinking Calibration For In-Context Learning And Prompt Engineering in context learning batch calibration rethinking prompt https://openreview.net/forum?id=XgH1wfHSX8 Competition Dynamics Shape Algorithmic Phases of In-Context Learning | OpenReview In-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely... in context learning competition dynamics shape algorithmic https://deepai.org/publication/understanding-in-context-learning-via-supportive-pretraining-data Understanding In-Context Learning via Supportive Pretraining Data | DeepAI Jun 26, 2023 - 06/26/23 - In-context learning (ICL) improves language models' performance on a variety of NLP tasks by simply demonstrating a handful of exa... in context learning understanding via supportive data https://openreview.net/forum?id=2SwHngthig Towards Offline Opponent Modeling with In-context Learning | OpenReview Opponent modeling aims at learning the opponent's behaviors, goals, or beliefs to reduce the uncertainty of the competitive environment and assist... in context learning towards offline opponent modeling https://openreview.net/forum?id=G7u4ue6ncT Implicit In-context Learning | OpenReview In-context Learning (ICL) empowers large language models (LLMs) to swiftly adapt to unseen tasks at inference-time by prefixing a few demonstration examples... in context learning implicit openreview https://openreview.net/forum?id=9QI3E2iaSD In-Context Learning of Energy Functions | OpenReview In-context learning is a powerful capability of certain machine learning models that arguably underpins the success of today's frontier AI models. However,... in context learning energy functions openreview https://openreview.net/forum?id=p3tSEFMwpG Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data |... While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts,... in context learning https://huggingface.co/papers/2306.15091 Paper page - Understanding In-Context Learning via Supportive Pretraining Data Join the discussion on this paper page in context learning paper understanding via supportive https://www.amazon.science/publications/adapting-llm-predictions-in-in-context-learning-with-data-priors Adapting LLM predictions in in-context learning with data priors - Amazon Science In-Context Learning (ICL) has enabled Large Language Models (LLMs) to excel as generalpurpose models in zero and few-shot task settings. However, since LLMs... in context learning adapting llm predictions https://openreview.net/forum?id=DoFtSA1qIB&referrer=%5Bthe%20profile%20of%20Eric%20Elmoznino%5D(%2Fprofile%3Fid%3D~Eric_Elmoznino1) Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain? | OpenReview Transformer models have shown considerable success in modeling predictive problems in diverse domains. It has been shown that they can efficiently learn... in context learning explicit knowledge https://openreview.net/forum?id=N2PwbxJ3o6 Towards Global Optimal Visual In-Context Learning Prompt Selection | OpenReview Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in... in context learning towards global optimal visual https://openreview.net/forum?id=yOhNLIqTEF Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study |... Large language models (LLMs) like GPT-4 and LLaMA-3 utilize the powerful in-context learning (ICL) capability of Transformer architecture to learn on the fly... in context learning understanding generalization https://openreview.net/forum?id=c8AGDmdCwO Understanding the Transient Nature of In-Context Learning: The Window of Generalization | OpenReview In-Context Learning (ICL) is one of the main mechanisms driving few shot learning capabilities of large language models (LLMs). A rich literatures explores the... in context learning understanding transient nature https://openreview.net/forum?id=ooXpTZYwXa Explore In-Context Learning for 3D Point Cloud Understanding | OpenReview With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential... in context learning point cloud explore 3d understanding https://arxiv.org/abs/2405.11751 [2405.11751] Asymptotic theory of in-context learning by linear attention Abstract page for arXiv paper 2405.11751: Asymptotic theory of in-context learning by linear attention in context learning asymptotic theory 2405 11751 https://openreview.net/forum?id=goi7DFHlqS Many-shot In-Context Learning | OpenReview Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight... in context learning many shot openreview https://openreview.net/forum?id=7t5DzaJOdB Task Diversity Shortens the In-Context Learning Plateau | OpenReview In-context learning (ICL) describes a language model's ability to generate outputs based on a set of input demonstrations and a subsequent query. To understand... in context learning task diversity plateau openreview https://openreview.net/forum?id=ekeyCgeRfC Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions... In order to understand the in-context learning phenomenon, recent works have adopted a stylized experimental framework and demonstrated that Transformers can... in context learning https://openreview.net/forum?id=Sin3j9XWX7 In-Context Learning for Latency Estimation | OpenReview Neural architectures must be computationally efficient for edge device deployment to perform well under hardware constraints. Current Hardware-Aware NAS... in context learning latency estimation openreview https://openreview.net/forum?id=AXer5BvRn1 Compositional Exemplars for In-context Learning | OpenReview Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task simply by... in context learning compositional exemplars openreview https://arxiv.org/abs/2501.16265 [2501.16265] Training Dynamics of In-Context Learning in Linear Attention Abstract page for arXiv paper 2501.16265: Training Dynamics of In-Context Learning in Linear Attention in context learning 2501 training dynamics linear https://aclanthology.org/2025.emnlp-main.929/ CrystalICL: Enabling In-Context Learning for Crystal Generation - ACL Anthology Ruobing Wang, Qiaoyu Tan, Yili Wang, Ying Wang, Xin Wang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. in context learning enabling crystal generation acl https://openreview.net/forum?id=LVF4P1NNwO&referrer=%5Bthe%20profile%20of%20Hwee%20Kuan%20Lee%5D(%2Fprofile%3Fid%3D~Hwee_Kuan_Lee1) Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers |... In-Context Learning (ICL) has been a powerful emergent property of large language models that has attracted increasing attention in recent years. In contrast... in context learning https://arxiv.org/abs/2410.09298 [2410.09298] DeepOSets: Non-Autoregressive In-Context Learning with Permutation-Invariance... Abstract page for arXiv paper 2410.09298: DeepOSets: Non-Autoregressive In-Context Learning with Permutation-Invariance Inductive Bias in context learning 2410 09298 non autoregressive https://openreview.net/forum?id=B50OF0Fc6O What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and... In-Context Learning (ICL) ability has been found efficient across a wide range of applications, where the Large Language Models (LLM) learn to complete the... what and how in context learning bayesian model averaging https://openreview.net/forum?id=rfCtCcPuSt Probing the Decision Boundaries of In-context Learning in Large Language Models | OpenReview In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models... in context learning large language models the decision https://aclanthology.org/2025.findings-emnlp.1353/ OptiSeq: Ordering Examples On-The-Fly for In-Context Learning - ACL Anthology Rahul Atul Bhope, Praveen Venkateswaran, K. R. Jayaram, Vatche Isahagian, Vinod Muthusamy, Nalini Venkatasubramanian. Findings of the Association for... on the fly in context learning https://aclanthology.org/2024.trustnlp-1.12/ HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality... Yihao Fang, Stephen Thomas, Xiaodan Zhu. Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024). 2024. in context learning https://openreview.net/forum?id=2J8xnFLMgF Why Larger Language Models Do In-context Learning Differently? | OpenReview Large language models (LLM) have emerged as a powerful tool for many AI problems and are deeply involved in many aspects of human activity. One important... in context learning language models larger differently openreview https://openreview.net/forum?id=YXewbZ8FgU Let the Rule Speak: Enhancing In-context Learning Debiasing with Interpretability | OpenReview In-context learning, which allows large language models to perform diverse tasks with a few demonstrations, is found to have imbalanced per-class prediction... in context learning the rule https://openreview.net/forum?id=xVRRNWo9j9 Prompting Wireless Networks: Reinforced In-Context Learning for Power Control | OpenReview To manage and optimize constantly evolving wireless networks, existing machine learning (ML) based studies operate as black-box models, leading to increased... in context learning wireless networks power control prompting reinforced https://openreview.net/forum?id=xSDqxxILWg Rethinking Invariance in In-context Learning | OpenReview In-Context Learning (ICL) has emerged as a pivotal capability of auto-regressive large language models, yet it is hindered by a notable sensitivity to the... in context learning rethinking invariance openreview https://openreview.net/forum?id=IWxPi5Ha7C Provable Benefits of Task-Specific Prompts for In-context Learning | OpenReview The in-context learning capabilities of modern language models have motivated a deeper mathematical understanding of sequence models. A line of recent work has... in context learning benefits of task specific provable https://openreview.net/forum?id=lHj-q9BSRjF Data Distributional Properties Drive Emergent In-Context Learning in Transformers | OpenReview Large transformer-based models are able to perform in-context few-shot learning, without being explicitly trained for it. This observation raises the question:... in context learning data distributional properties drive https://openreview.net/forum?id=C9CSaTR1iA In-context Learning in Presence of Spurious Correlations | OpenReview Large language models exhibit a remarkable capacity for in-context learning, where they learn to solve tasks given a few examples. Recent work has shown that... in context learning spurious correlations presence openreview https://openreview.net/forum?id=BnZaDzYEeK&referrer=%5Bthe%20profile%20of%20Zihao%20Xu%5D(%2Fprofile%3Fid%3D~Zihao_Xu2) Implicit In-context Learning | OpenReview in context learning implicit openreview https://openreview.net/forum?id=6xU0qcfda9&referrer=%5Bthe%20profile%20of%20Jiahao%20Zhang%5D(%2Fprofile%3Fid%3D~Jiahao_Zhang12) Instruct Me More! Random Prompting for Visual In-Context Learning | OpenReview Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context... in context learning instruct me more random prompting https://aclanthology.org/2022.emnlp-main.759/ Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? - ACL Anthology Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. Proceedings of the 2022 Conference on Empirical Methods... in context learning https://aclanthology.org/2025.emnlp-main.219/ In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language... Nathan Roll, Calbert Graham, Yuka Tatsumi, Kim Tien Nguyen, Meghan Sumner, Dan Jurafsky. Proceedings of the 2025 Conference on Empirical Methods in Natural... in context learning https://openreview.net/forum?id=lcTFm4LIRR Associative memory inspires improvements for in-context learning using a novel attention residual... Large language models (LLMs) demonstrate an impressive ability to utilise information within the context of their input sequences to appropriately respond to... in context learning https://openreview.net/forum?id=VbmqcoHpGT Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data |... While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts,... in context learning