Large Language Models

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Large Language

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Large Language Model Performance

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Large Language Models(Llms

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Large Language Model Output

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Large Language Model Calls

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847 papers

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A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM $Δ$ Integration into Upcycled MoE
May 18, 2026Hao Zhou, Tianhao Li, Zhijun Wang +6Multilingual Large Language Models Mixture-Of-Expert Language Models
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Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
May 17, 2026Yuxuan Lu, Ziyi Wang, Yingzhou Lu +12Call Large Language Model Calls
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May 17, 2026Zehan Sun, Dingfan Chen, Songze LiLanguage Model Evasion Attacks Large Language Model Performance
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Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference
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Evaluating Chinese Ambiguity Understanding in Large Language Models
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Toward LLMs Beyond English-Centric Development
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COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion
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Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
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Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents
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LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling
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When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
May 12, 2026Shixing Yu, Promit Ghosal, Kyra GanToken-Level Uncertainty Large Language Model Output
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May 12, 2026Seokwon Jung, Alexander Rubinstein, Arnas Uselis +2Large Language Entities
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The First Drop of Ink: Nonlinear Impact of Misleading Information in Long-Context Reasoning
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Repeated-Token Counting Reveals a Dissociation Between Representations and Outputs
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Where Larger Models Excel: The Primacy of Constraint-Guided Reasoning
May 9, 2026Guan-Yi Lin, Hen-Hsen HuangReasoning Benchmark Large Behavior Model
The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans
May 9, 2026Odysseas S. Chlapanis, Orfeas Menis Mastromichalakis, Christos H. PapadimitriouLarge Language Model Performance Contextual Grounding

May 18, 2026 · cs.CLJ/K move · Enter open · S save

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM $Δ$ Integration into Upcycled MoE

Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She+5

National Key Laboratory for Novel Software Technology, Nanjing University · Tongyi Lab, Alibaba Group · Zhejiang University

Expanding Large Language Models~(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training~(CPT) and data-intensive alignment. While recent data-free merging techniques attempt to bypass alignment by fusing a multilingual CPT-enhanced model with its instruct counterpart, they are plagued by a critical trade-off: mitigating parameter conflicts to preserve original abilities inevitably dilutes new language acquisition, and vice-versa. To resolve this conflict, we introduce \method, which upcycles a dense model into a Mixture-of-Experts~(MoE) architecture, allocating different experts to different languages. Alignment ability is then transferred by grafting a MoE-expanded parameter delta~(

Δ_{\text{post}}

) to the CPT-enhanced base model, bypassing the complex alignment phase. Experiments demonstrate \method's superiority even against baselines with similar FLOPs or number of parameters; it improves performance on expanded languages while effectively preserving original capabilities. We further show our approach is highly applicable across different models and Post-training deltas.

Multilingual Large Language Models Mixture-Of-Expert Language Models Large Language Mixture-of-Experts Models Expansions Feature Alignment

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