6 citations · 13 across the 15 of their papers we have counts for
5 papers · 1 filter
Law in Silico: Simulating Legal Society with LLM-Based Agents
Yiding Wang, Yuxuan Chen, Fanxu Meng +3
Since real-world legal experiments are often costly or infeasible, simulating legal societies with Artificial Intelligence (AI) systems provides an effective alternative for verify…
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…
LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades
Yanan Li, Fanxu Meng, Muhan Zhang +3
As Large Language Models (LLMs) are frequently updated, LoRA weights trained on earlier versions quickly become obsolete. The conventional practice of retraining LoRA weights from…
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…
TransMLA: Multi-Head Latent Attention Is All You Need
Fanxu Meng, Pingzhi Tang, Xiaojuan Tang +3
In this paper, we present TransMLA, a framework that seamlessly converts any GQA-based pre-trained model into an MLA-based model. Our approach enables direct compatibility with Dee…