Publications

2023

1. arXiv

   DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

   Shengbin Yue, Wei Chen, Siyuan Wang, and 8 more authors

   2023

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   We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models’ ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available here.

   @misc{yue2023disclawllm,
     title = {DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services},
     author = {Yue, Shengbin and Chen, Wei and Wang, Siyuan and Li, Bingxuan and Shen, Chenchen and Liu, Shujun and Zhou, Yuxuan and Xiao, Yao and Yun, Song and Huang, Xuanjing and Wei, Zhongyu},
     year = {2023},
     eprint = {2309.11325},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
     doi = {10.48550/arXiv.2309.11325},
   }

2. arXiv

   Efficiently Visualizing Large Graphs

   Xinyu Li, Yao Xiao, and Yuchen Zhou

   2023

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   Most existing graph visualization methods based on dimension reduction are limited to relatively small graphs due to performance issues. In this work, we propose a novel dimension reduction method for graph visualization, called t-Distributed Stochastic Graph Neighbor Embedding (t-SGNE). t-SGNE is specifically designed to visualize cluster structures in the graph. As a variant of the standard t-SNE method, t-SGNE avoids the time-consuming computations of pairwise similarity. Instead, it uses the neighbor structures of the graph to reduce the time complexity from quadratic to linear, thus supporting larger graphs. In addition, to suit t-SGNE, we combined Laplacian Eigenmaps with the shortest path algorithm in graphs to form the graph embedding algorithm ShortestPath Laplacian Eigenmaps Embedding (SPLEE). Performing SPLEE to obtain a high-dimensional embedding of the large-scale graph and then using t-SGNE to reduce its dimension for visualization, we are able to visualize graphs with up to 300K nodes and 1M edges within 5 minutes and achieve approximately 10% improvement in visualization quality. Codes and data are available here.

   @misc{li2023efficiently,
     title = {Efficiently Visualizing Large Graphs},
     author = {Li, Xinyu and Xiao, Yao and Zhou, Yuchen},
     year = {2023},
     eprint = {2310.11186},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     doi = {10.48550/arXiv.2310.11186},
   }