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cs.CL2025
LooGLE v2: Are LLMs Ready for Real World Long Dependency Challenges?
Ziyuan He, Yuxuan Wang, Jiaqi Li +2
Large language models (LLMs) are equipped with increasingly extended context windows recently, yet their long context understanding capabilities over long dependency tasks remain f…
cs.LG2025
TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference
Xiaojuan Tang, Fanxu Meng, Pingzhi Tang +4
Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value states into a low-rank latent vector, caching only this vector to reduce memory. In tensor parall…
cs.CL2025★ 1 cited
LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Yansheng Mao, Yufei Xu, Jiaqi Li +5
Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-cont…