MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Legal judgment prediction offers a compelling method to aid legal
practitioners and researchers. However, the research question remains
relatively under-explored: Should multiple defendants and charges be treated
separately in LJP? To address this, we introduce a new dataset namely
multi-person multi-charge prediction (MPMCP), and seek the answer by evaluating
the performance of several prevailing legal large language models (LLMs) on
four practical legal judgment scenarios: (S1) single defendant with a single
charge, (S2) single defendant with multiple charges, (S3) multiple defendants
with a single charge, and (S4) multiple defendants with multiple charges. We
evaluate the dataset across two LJP tasks, i.e., charge prediction and penalty
term prediction. We have conducted extensive experiments and found that the
scenario involving multiple defendants and multiple charges (S4) poses the
greatest challenges, followed by S2, S3, and S1. The impact varies
significantly depending on the model. For example, in S4 compared to S1,
InternLM2 achieves approximately 4.5% lower F1-score and 2.8% higher LogD,
while Lawformer demonstrates around 19.7% lower F1-score and 19.0% higher LogD.
Our dataset and code are available at
https://github.com/lololo-xiao/MultiJustice-MPMCP.
