## 4 Experimental Evaluation
### Experiment Analysis
In Figure 6, the experimental results demonstrate that the highest translation accuracy is achieved with the high fine-tuning strategy, as the abundant data from high-resource languages enables the model to capture more context and complex linguistic structures, thereby enhancing its translation performance. In contrast, on low-resource datasets such as IWSLT 17 and XGLUE 20, the low fine-tuning strategy resulted in significantly lower accuracy, with an average decrease of 3.9%. The hybrid fine-tuning approach, although showing performance improvement over low fine-tuning, did not match the effectiveness of the high fine-tuning. This suggests that while hybrid fine-tuning benefits from the richer linguistic features learned from high-resource languages, it is not yet fully optimized to transfer this knowledge effectively to low-resource language tasks. Nonetheless, the improvements observed indicate that hybrid fine-tuning aids in enhancing the model's ability to understand complex language phenomena, such as domain-specific language comprehension in technical fields like computerized English. In order to deeply analyze the performance of the LightChatGLM model at different compression levels, we designed a series of compression experiments to test core indicators such as model size, inference time, and translation accuracy, as shown in Table 4.  
Figure 5: Experimental average results of cross-language migration with different fine-tuning methods on three different datasets. Figure 6: Effectiveness of hybrid data fine-tuning method on different resource language datasets. The experimental results demonstrate that as the compression level increases, the model's resource consumption decreases significantly, but this is accompanied by a corresponding loss in performance. While mixed compression techniques effectively reduce resource usage and inference time, higher compression levels can lead to a marked degradation in model performance, particularly in tasks requiring complex language understanding. The performance trade-offs observed at different compression levels highlight the importance of selecting an appropriate compression strategy based on the specific application scenario. This ensures that model accuracy is preserved as much as possible while optimizing resource efficiency. Furthermore, these findings introduce a critical challenge for future research: how to maintain the model's robust generalization capabilities for language features under extreme compression. To further validate the effectiveness of the combination of techniques in LightChatGLM, we designed an ablation comparison experiment to systematically analyze the impact of three core techniques: parallel training, mixed compression, and hybrid data fine-tuning. Each technique was applied individually and in combination to assess their respective contributions. The core components of LightChatGLM were decomposed, based on which we structured the ablation experiments. * Baseline Model: The baseline model is ChatGLM-6B. * Parallel Training Algorithm: Parallel training was applied to assess its impact on training speed and model performance. * Mixed Compression: Only mixed compression techniques were used to evaluate their effect on model size, inference time, and performance. * Hybrid Data Fine-Tuning: The impact of hybrid data fine-tuning on translation accuracy was assessed. * Parallel Training + Mixed Compression: A combination of parallel training and mixed compression, excluding hybrid fine-tuning, was tested. * Parallel Training + Hybrid Fine-Tuning: Parallel training was combined with hybrid fine-tuning, without applying mixed compression. * Mixed Compression + Hybrid Fine-Tuning: Mixed compression and fine-tuning were applied together, without parallel training. * LightChatGLM: The complete model was tested with all three core combined techniques. Table 5 shows the results of the above ablation experiments.