| 1 |
Neurograf v1: An End-to-End Romanian-First Language Model Family |
提出Neurograf v1以解决罗马尼亚低资源语言模型训练问题 |
instruction following |
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| 2 |
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training |
提出CIG-SFT以解决后训练模型泛化能力不足的问题 |
large language model |
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| 3 |
Task Context Shapes Component Roles in Language Models |
提出任务上下文视角以解析语言模型组件角色 |
large language model |
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| 4 |
Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction |
提出UPTURN框架以解决LLM自我纠错问题 |
large language model |
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| 5 |
Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding |
提出基于信息和几何分析的多层隐藏状态选择方法以优化LLM的推测解码 |
large language model |
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| 6 |
Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion |
提出SAFE-QE以解决大语言模型的幻觉术语问题 |
large language model |
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| 7 |
Towards the Roof of the World: NLP Progress in Tibetan Languages |
系统性调查西藏语言自然语言处理的研究进展 |
large language model |
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| 8 |
MoPrO-SD: Modular Prompt Optimization for Stance Detection |
提出MoPrO-SD框架以优化立场检测中的提示设计 |
large language model |
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| 9 |
Test-Time Memory Construction |
提出T$^2$Mem以解决长交互历史中的记忆构建问题 |
large language model |
✅ |
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| 10 |
Host-Aware Web Crawling for LLM Pretraining Data |
提出主机感知网络爬虫以优化LLM预训练数据收集 |
large language model |
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| 11 |
Evaluating LLM Creativity through Long-Tail Performance |
提出长尾性能评估方法以提升大语言模型创造力 |
large language model |
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