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20182026
most citedFederated Learning for Mobile Keyboard Prediction

1.1k citations · 1.3k across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

Evo: Evolving the Evolver through Weight-Harness Co-Evolution

Chejian Xu, Shujian Zhang, Wenxuan Zhou +5

Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improvements. Existing approaches t…

cs.CL2025

MUSIC: MUlti-Step Instruction Contrast for Multi-Turn Reward Models

Wenzhe Li, Shujian Zhang, Wenxuan Zhou +5

Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human…

cs.CL2025

Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process

Zhenyu Zhang, Shujian Zhang, John Lambert +6

Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches oft…

cs.CL2025

Eliciting Behaviors in Multi-Turn Conversations

Jing Huang, Shujian Zhang, Lun Wang +3

Identifying specific and often complex behaviors from large language models (LLMs) in conversational settings is crucial for their evaluation. Recent work proposes novel techniques…

cs.CL2018★ 1.1k cited

Federated Learning for Mobile Keyboard Prediction

Andrew Hard, Kanishka Rao, Rajiv Mathews +6

We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual…