# Accusations and Law Articles Prediction in the Field of Environmental Protection
## 5 Experiment
### Experiment Results  
#### 5.3.1 Comparison of External Knowledge Infused Cross Model Variationsc  
Table 3 showcases the efficacy of different model architectures in addressing charge and article prediction tasks. Fact-Only denotes the use of fact descriptions as the sole input for predictions. The Concatenate Attention model concatenates three distinct representation parts, enhancing its capability to discern complex interdependencies between legal facts and charges. However, this straightforward summation of representations did not perform as well as the cross-attention fusion mechanism employed in the EKICAN model, indicating the superior efficacy of more sophisticated attention architectures in handling complex relational data. In contrast, EKICAN (w/o EK) marginally underperforms compared to EKICAN, which incorporates external knowledge and utilizes a multi-head attention mechanism for dot product fusion, achieving superior performance across most metrics. Notably, EKICAN excels in Macro-F1 for charge predictions and Micro-F1 for article predictions, demonstrating the significant benefits of integrating external knowledge to improve predictive accuracy. Architectural variations of these models are illustrated in Figure 5. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**BLEU**} & \multicolumn{3}{c}{**ROUGE**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1**} \\  & **B-1** & **B-2** & **B-N** & & & **R-1** & & **R-2** & & \\ \hline KeyWords & 28.7 & 15.7 & 6.3 & 34.1 & 18.3 & 28.2 & 52.5 & 61.2 & 63.8 \\ WisdomInterrogatory & 32.9 & 23.1 & 11.7 & 53.3 & 26.5 & 46.9 & 86.2 & 85.1 & 85.6 \\ Fuzi & 30.6 & 21.1 & 9.9 & 47.3 & 25.1 & 42.7 & 83.4 & 85.5 & 84.5 \\ LawGPT & 29.8 & 20.1 & 9.4 & 46.7 & 24.4 & 41.2 & 81.6 & 84.3 & 82.9 \\ ChatLaw & 30.3 & 20.4 & 9.7 & 44.3 & 22.8 & 39.1 & 82.3 & 83.2 & 82.8 \\ ChatGLM2 & 36.8 & 26.3 & 14.2 & **62.3** & 32.3 & 49.1 & **92.5** & 90.4 & 91.3 \\ GPt3.5 & 33.3 & 23.9 & 12.1 & 57.0 & 27.6 & 47.9 & 87.1 & 86.5 & 86.8 \\ GPT4 & **37.1** & **26.8** & **14.5** & 61.9 & **34.2** & **49.7** & 92.3 & **91.7** & **92.0** \\ \hline \hline \end{tabular}  
* **Bold numbers** indicate the best performance for each metric.  
\end{table} 
Table 2: Comparison of results between keyword extraction and large model generation methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{3}{c}{**Charge**} & \multicolumn{6}{c}{**Article**} \\  & **Ma-P** & **Ma-R** & **Ma-F1** & **Mi-P** & **Mi-R** & **Mi-F1** & **Ma-P** & **Ma-R** & **Ma-F1** & **Mi-P** & **Mi-R** & **Mi-F1** \\ \hline Fact-Only & 85.7 & 84.2 & 84.9 & 92.3 & 90.5 & 91.9 & 61.1 & 59.6 & 58.2 & 83.0 & 82.3 & 82.6 \\ Att-None & 87.0 & 85.5 & 86.2 & 96.4 & 95.0 & 94.8 & 73.2 & 66.0 & 60.1 & 84.8 & 84.3 & 84.2 \\ Single-Head Att & 89.2 & 88.3 & 88.8 & 96.5 & 96.7 & 96.4 & 73.6 & 67.3 & 67.3 & 86.6 & 85.3 & 85.9 \\ Concatenate Att & 91.3 & 90.5 & 90.9 & 98.2 & 98.2 & 97.1 & 74.1 & 67.7 & 62.4 & 88.1 & 86.7 & 87.4 \\ EKIC Att(w/o EK) & 91.0 & 90.1 & 90.6 & 96.1 & 95.9 & 95.7 & 62.4 & 61.3 & 61.8 & 87.7 & 86.4 & 86.8 \\ EKIC Att & **92.3** & **91.5** & **91.9** & **98.7** & **98.6** & **98.6** & **76.3** & **69.8** & **64.3** & **88.7** & **88.2** & **88.4** \\ \hline \hline \end{tabular}  
* **Bold numbers** indicate the best performance for each metric.  
\end{table} 
Table 3: Experiments on different models. We presents the analysis of the results derived from our experiments, specifically focusing on the trends exhibited across different architectural setups over training epochs. This is depicted in Figure 6, which illustrates the evolution of Macro-F1 scores across training epochs for various models engaged in charge and article prediction tasks. We can conclude the following: The Concatenate Attention model demonstrates consistent improvement over time, underscoring its ability to effectively integrate multiple information streams.