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20242026
most citedFederated Learning Priorities Under the European Union Artificial Intelligence Act

9 citations · 16 across the 27 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

Sparse Readout Prism: Explaining Logit-Lens Scores in Features Instead of Tokens

Matteo He, William F. Shen, Xinchi Qiu +1

A language model's prediction of its next token develops across layers, and lens methods track this process by decoding intermediate hidden states into tokens. But a lens reading r…

cs.CL2026

Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems

Nurbek Tastan, Alex Iacob, Lorenzo Sani +4

Multi-agent systems can solve complex tasks through collaboration between multiple Large Language Model agents. Existing collaboration frameworks typically operate in either a para…

cs.CL2026

EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints

Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3

Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continuously generated data from sm…

cs.CL2025

Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages

Wanru Zhao, Yihong Chen, Royson Lee +4

Pre-trained large language models (LLMs) have become a cornerstone of modern natural language processing, with their capabilities extending across a wide range of applications and…

cs.CL202417 cited

Small Language Models: Survey, Measurements, and Insights

Zhenyan Lu, Xiang Li, Dongqi Cai +5

Small language models (SLMs), despite their widespread adoption in modern smart devices, have received significantly less academic attention compared to their large language model…