## 4 Experiment and Result
### Couplet Generation  
In the PCcG framework, we have constructed a progressive prompt that requires only the input of personalized needs, such as the embedded words or the creative intent of the couplet, following the specified format. To evaluate the framework, we created various combinations of personalized needs and generated three pairs of couplets under each combination using different methods. Figure 5 presents the results between the base model and our framework. Figure 6 presents the results of the two latest models under the same conditions. Compared to other models, our approach demonstrates greater flexibility and appropriateness in handling embedded words. The couplets generated by other models tend to embed the specified words in the same position in both clauses, often placing them as the first word of each clause. This pattern is unreasonable. We hypothesize that the base model's logic for generating couplets with embedded words is as follows: it takes the given words, places them as the first word of the antecedent and subsequent clauses, and then generates the rest of the couplet by inferring forward. This process aligns with the Next Sentence Prediction (NSP) task used during the model's training. While the base model technically fulfills the needs of embedded words, it inadvertently adds an implicit constraint that the embedded words must appear in the first position, significantly limiting the diversity of the couplets and failing to meet practical needs. In contrast, our model avoids this issue, allowing embedded words to appear in various positions, which better aligns with real-world requirements. For example, in Figures 5 and 6, when the personalized need is \"embedded words (\(\overline{\Xi}\) (snow) and \(\overline{\Xi}\) (plum)\", all methods place these words in the first of the two clauses and each clause just consists of a single part, except for ours, which places the words more flexibly (i.e., in the middle of the couplet) and each clause can be made up of three parts. It is more flexible, complex, and elegant. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline learning\_rate & \(2\times 10^{-4}\) \\ optim & adamw\_8 bit \\ weight\_decay & 0.01 \\ lr\_scheduler\_type & linear \\ seed & 3401 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SFT parameter settings. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline r & 16 \\ lora\_alpha & 16 \\ lora\_dropout & 0 \\ bias & none \\ random\_state & 3401 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LoRA parameter settings. In terms of creative intent personalization, our model exhibits greater diversity in both word choice and sentence structure, aligning more closely with the creative essence of  
Figure 5: Example of generation results. Except for those in the “Translation” section, where couplets with line breaks are distinguished using double quotation marks, each line represents a clause of the couplet. Figure 6: The results of the two latest models under the same conditions. couplet composition. Observations reveal that the couplets generated by the base model exhibit high redundancy in word choice, often repeating words like \"#\" (spring) in the antecedent clauses. The sentence structures are also monotonous, typically producing simple couplets without punctuation. We hypothesize that this is due to the base model's lack of specialized knowledge about couplet structures, causing it to favor common and straightforward patterns. While the couplets generated by the base model satisfy the creative intent requirements on a basic level, they lack the artistic diversity and creativity essential for couplet composition. In contrast, our approach generates a more diverse vocabulary and demonstrates greater creativity in sentence structures. Overall, our model outperforms the base model in both embedded words and creative intent personalization. It not only satisfies the specified personalized needs but also generates higher quality couplets.