arXiv AI June 2, 2026 By Hao Xu, Rite Bo, Fausto Giunchiglia, Yingji Li, Rui Song

Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval

arXiv:2606. 00014v1 Announce Type: cross Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe.

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llms

arXiv AI

Jun 30

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

arXiv:2606. 29407v1 Announce Type: cross Abstract: There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).

By Xiao You, Tianwei Yan, Shan Zhao

llms nlp

Hugging Face Trending Papers

Sep 15

Long-Context Demonstration Selection Using State Space Models

We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This problem is closely related to in-context learning and...

llms rag nlp efficiency

arXiv Machine Learning

Sep 17

Long-Context Demonstration Selection Using State Space Models

The paper addresses the challenge of selecting demonstrations for long-context language model queries, where transformer inference costs grow quadratically with sequence length. It proposes two algorithms that distill transformer behavior into state space models (SSMs) with linear inference time, partitioning transformer layers into groups and estimating separate SSMs for each. The distilled SSMs achieve less than 0.7% approximation error, and in downstream tasks they reduce FLOPs by 14.2× while improving accuracy by 6.48% compared to baseline methods.

By Ziniu Zhang, Zhenshuo Zhang, Ruoxuan Xiong, Gene Cooperman, Hongyang R. Zhang

llms rag nlp efficiency

arXiv AI

Jun 9

Language-based Trial and Error Falls Behind in the Era of Experience

arXiv:2601. 21754v3 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.

By Haoyu Wang, Guozheng Ma, Shugang Cui, Yilun Kong, Haotian Luo, Li Shen, Mengya Gao, Yichao Wu, Xiaogang Wang, Dacheng Tao

llms agents reinforcement-learning fine-tuning

arXiv AI

Jul 7

Context Tuning for In-Context Optimization

arXiv:2507. 04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates.

By Jack Lu, Ryan Teehan, Zhenbang Yang, Mengye Ren

llms benchmarks

arXiv Machine Learning

Jun 4

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning

arXiv:2606. 04434v1 Announce Type: cross Abstract: Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks.

By Niloufar Alipour Talemi, Hossein Kashiani, Fatemeh Afghah

llms efficiency multimodal benchmarks

One email a morning, machine-written

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LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

Long-Context Demonstration Selection Using State Space Models

Long-Context Demonstration Selection Using State Space Models

Language-based Trial and Error Falls Behind in the Era of Experience

Context Tuning for In-Context Optimization

Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning