Token-level Preference Self-Alignment Optimization for Multi-style Outline Controllable Generation
Authors: Zihao Li, Xuekong Xu, Ziyao Chen, Lixin Zou, Ethanhjwu, Qiang Chen, Chenliang Li
Accepted at: acl25

Token-level Preference Self-Alignment Optimization for Multi-style Outline Controllable Generation

Zihao Li1*, Xuekong Xu1, Ziyaq Chen2, Lixin Zou1, Haijun Wu2, Qiang Chen2, Chenliang Li1†,

1 Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University
2Tencent WeChat, Guangzhou, China
{zihao.li, zoulixin, cllee}@whu.edu.cn
{yateschen, ethanhjwu, ethanqchen}@tencent.com

Abstract

Multi-style outline controllable generation is crucial for multiple applications, including document semantic structuring and retrieval-augmented generation. The great success of preference alignment approaches encourages their application in controllable generation tasks. However, these attempts encounter several limitations: (1) response pair requirements, (2) substantial computation costs, and (3) insufficient exploitation of fine-grained preference signals. To address these problems, we propose a token-level preference self-alignment optimization, named TKPO, for outline controllable generation. TKPO extends the Bradley-Terry model from pair-wise to list-wise comparison, which is further applied at the token level for fine-grained preference signal utilization. In comparison to the representative methods, e.g., DPO, TKPO does not require response pairs; instead, we propose a controllable attributes-driven method to construct reject samples for self-alignment. Additionally, TKPO optimizes only the base model, thereby avoiding additional memory usage and substantial computational costs. We curate two outline controllable generation datasets with regard to language style and level-of-detail. Extensive experiments demonstrate that TKPO outperforms DPO by up to 19.28% in performance while requiring only 56.25% in training time. We release the code and datasets resources at https://github.com/WHUIR/TKPO.

1 Introduction

Given an article, outline controllable generation aims to output a hierarchical outline with respect to the content of the articles and the controllable conditions, as shown in Figure 1. Existing attempts to outline generation can be classified into two research lines: (1) segmentation and summarization (Sun et al., 2022); (2) prompt-driven generation (Yang et al., 2023; Li et al., 2024). The first category divides articles into distinct parts based on paragraph coherence or topical consistency and then generates a summary or topic for each segment. Prompt-driven approaches focus on tailored prompts fine-tuning for outline generation.

Nevertheless, it is noteworthy that the exploration of this task is insufficient, and none of the research focuses on the multi-style outline controllable generation, which is significant for various downstream applications, such as personalized article summarization, semantic structuralization, and retrieval augmented generation. To fill this gap, we first define two distinct outline styles, i.e., language styles (literary vs. objective)1 and

![An example of outline language style controllable generation. Given the articles and prompts with specified control attributes, e.g., objective vs. literary, the LLMs are required to generate outlines consistent with the control attribute.](page_1012_370_377_377.png)

Figure 1: An example of outline language style controllable generation. Given the articles and prompts with specified control attributes, e.g., objective vs. literary, the LLMs are required to generate outlines consistent with the control attribute.

*Work done during an internship at Tencent WeChat
†Corresponding author.

1The literary outlines aim to provide a high-level abstrac-
level-of-detail (concise vs. moderate vs. comprehensive)2 (ref. Table 9 to Table 16 in Appendix H for details). Subsequently, two datasets containing multiple distinct outline styles are curated, respectively.

Inspired by the promising results of preference alignment algorithms in conditional generation (Ouyang et al., 2022; Wang et al., 2024), we are committed to applying these methods for outline controllable generation. However, existing preference alignment solutions encounter three limitations: (1) response pair requirements, (2) extensive computation costs, and (3) inadequate utilization of fine-grained preference signals, hindering their further development. More concretely, the majority of strategies necessitate a response pair (an accepted and a declined response) for preference comparison and alignment. This requirement poses significant challenges in the real world compared to the simpler single-response dataset construction. Besides, these methods, such as DPO (Wang et al., 2024), involve at least two models (i.e., reference model and policy model) during training, incurring additional memory usage and computational costs. Furthermore, we argue that sentence-level alignment limits the LLMs’ discrimination capability (Guo et al., 2023), as the preferences are more susceptible to the specific words (e.g., toxic terms) (Christopoulou et al., 2024). Hence, fine-grained token-level signals distinguishing between good and bad responses are more appropriate for precise training and optimization.

To address these issues, we propose a token-level preference self-alignment optimization, dubbed TKPO. Compared to existing token-level preference alignment attempts (Liu et al., 2024a; Zeng et al., 2024), our TKPO is response pair-free and efficient, enabling precisely controllable generation. Specifically, TKPO expands the Bradley-Terry model (Bradley and Terry, 1952) from the pair-wise into the list-wise comparison (i.e., one accepted instance vs. multi-rejected instances), which is further applied at the token level. TKPO eliminates the necessity for paired response data, instead, we propose a controllable attribute-driven method, in which the fundamental capability of LLMs is exploited to construct reject samples for self-alignment. In comparison to the representative preference alignment methods, TKPO only optimizes the base model, avoiding additional memory usage and substantial computational cost. Theoretical analysis demonstrates that optimizing the TKPO is equivalent to optimizing the upper bound of the optimal policy model under the Bradley-Terry framework. Additionally, our TKPO demonstrates superior capability for optimizing the hard tokens for fine-grained alignments and exhibits notable diversity in the generation.

We devise two metrics for the outline language style and level-of-detail controllable generation evaluation. Extensive experiments illustrate that our method outperforms DPO with only 73.55% memory usage. To encapsulate, the main contributions of this paper include the following:

• We propose a multi-style and hierarchical outline controllable generation task. To this end, two datasets with regard to outline language style (literary vs. objective) and level-of-detail (concise vs. moderate vs. comprehensive) are curated, respectively.

• We propose a token-level preference self-alignment framework, named TKPO, which is a response pair-free and efficient solution against existing recipes. Additionally, TKPO enhances diversity in generation and facilitates more precise alignment at a more fine-grained token level.

• Experimental results illustrate the superiority of our method on both controllable generation performance and training efficiency compared to representative preference alignment optimization solutions, such as DPO. Theoretical analysis demonstrates the merits of our TKPO on hard token optimization and diversity enhancement.

2 Methodology

2.1 Preliminary

Preference alignment aims to steer the LLMs to generate responses that are satisfactory and acceptable to humans (Leike et al., 2018), ensuring their security, honesty, and helpfulness. Reinforcement learning from human feedback (RLHF) (Ouyang
Direct Preference Optimization
Prompt: “Generate a structured and hierarchical outline for the following Article.”

![Diagram showing Direct Preference Optimization: Response Preference Pair (y_w > y_l) → Maximum likelihood → Final LLM](page_120_120_480_180.png)

Sentence-level Alignment:
\[
L_{DPO}(\pi_\theta; \pi_{ref}) = -\mathbb{E}_{(x, y_w, y_l) \sim \mathcal{D}} \left[ \log \sigma \left( \beta \log \frac{\pi_\theta(y_w|x)}{\pi_{ref}(y_w|x)} - \beta \log \frac{\pi_\theta(y_l|x)}{\pi_{ref}(y_l|x)} \right) \right]
\]

Token-Level Preference Optimization
Prompt: “Generate a structured and hierarchical outline for the following Article.”
Requirements: <Control Attribute>

![Diagram showing Token-Level Preference Optimization: Token Preference List (y_{tw} > y_{tl}) → Maximum likelihood → Final LLM](page_120_360_480_180.png)

Token-level Alignment:
\[
L_{TKPO}(\pi_\theta ) = -\mathbb{E}_{(x, y) \sim \mathcal{D}} \log \frac{\exp(\pi_\theta(y_{tw}|x)/\tau)}{\exp(\pi_\theta(y_{tw}|x)/\tau) + \sum_{l \in L} \exp(\pi_\theta(y_{tl}|x)/\tau)}
\]

Figure 2: Comparison between the paradigms of DPO and our TKPO. The bar plot illustrates that the TKPO outperforms the DPO with only 73.55% GPU memory usage and 56.25% one-step training time (conducted on the 8 × Nvidia Tesla V100 SXM2) for outline language style controllable generation.

et al., 2022) is an effective method for preference alignment. However, it experiences significant complexity and training instability (Song et al., 2023). To address these problems, Direct Preference Optimization (DPO) (Wang et al., 2024) is proposed further. Rather than learning an explicit reward model, the DPO algorithm straightforwardly optimizes the policy by reparameterizing the reward function \( r(\cdot) \) using a closed-form expression in a supervised manner (ref. Appendix B.1):

\[
r(x, y) = \beta \log \frac{\pi_\theta(y|x)}{\pi_{ref}(y|x)} + \beta \log Z(x)
\] (1)

where \( Z(x) = \sum_y \pi_{ref}(y|x) \exp(\frac{1}{\beta}(r(x, y))) \) is the partition function. \( \beta \) controls the deviation of the language model policy \( \pi_\theta \) from the base reference policy \( \pi_{ref} \).

By incorporating the reward function (Eq. (1)) into the Bradley-Terry (BT) objective formula (Bradley and Terry, 1952),

\[
p(y_w > y_l|x) = \frac{1}{1 + \exp(r(x, y_w) - r(x, y_l))} \\
= \sigma(r(x, y_w) - r(x, y_l))
\] (2)

we, thereby, cancel out the partition function \( Z(x) \), resulting in the objective of DPO with reverse KL divergence below:

\[
-\mathbb{E}_{(x, y_w, y_l) \sim \mathcal{D}} \left[ \log \sigma \left( \beta \log \frac{\pi_\theta(y_w|x)}{\pi_{ref}(y_w|x)} - \beta \log \frac{\pi_\theta(y_l|x)}{\pi_{ref}(y_l|x)} \right) \right]
\] (3)

where \( \sigma(\cdot) \) is the sigmoid function. \( y_w \) and \( y_l \) are preference pairs consisting of the approved response and declined response with regard to the input \( x \).

2.2 Token-level Alignment

Despite the promising results of the DPO, the training requirements (\emph{i.e.}, \( y_w \) and \( y_l \)) for response pairs and substantial training costs (\emph{i.e.}, \( \pi_\theta \) and \( \pi_{ref} \) are involved) impede the widespread application in practical scenarios. To overcome this problem, we adopt preference alignment on the token level for a more precise controllable generation, as shown in Figure 2. Specifically, TKPO first extends the Bradley-Terry model (Eq.(2)) from a pairwise comparison to a list-wise framework, \emph{i.e.}, increasing the probability of accepted instances \( y_w \)
while simultaneously decreasing the probability of all declined instances \( \{y_\ell| \ell = 1, ..., K\} \). It can be formalized as:

\[
p\left(y_w \succ \{y_\ell\}_{\ell=1}^K|x\right) = \prod_{\ell=1}^K p(y_w \succ y_\ell|x)
\]
\[
= \prod_{\ell=1}^K \frac{\exp(y_w|x)}{\exp(y_w|x) + \exp(y_\ell|x)}
\] (4)

Substituting Eq. (1) into Eq. (4) we obtain (ref. Appendix B.2 for detail):

\[
p^*\left(y_w \succ \{y_\ell\}_{\ell=1}^K|x\right)
= \prod_{\ell=1}^K \frac{\exp\left(\beta \log \frac{\pi_\theta^*(y_w|x)}{\pi_{\text{ref}}(y_w|x)}\right)}{\exp\left(\beta \log \frac{\pi_\theta^*(y_w|x)}{\pi_{\text{ref}}(y_w|x)}\right) + \exp\left(\beta \log \frac{\pi_\theta^*(y_\ell|x)}{\pi_{\text{ref}}(y_\ell|x)}\right)}
< \frac{1}{1 + \sum_{\ell=1}^K \exp\left(\beta \log \frac{\pi_\theta^*(y_\ell|x)}{\pi_{\text{ref}}(y_\ell|x)} - \beta \log \frac{\pi_\theta^*(y_w|x)}{\pi_{\text{ref}}(y_w|x)}\right)}
\] (5)

Following (Meng et al., 2024; Hong et al., 2024), we remove the reference model \( \pi_{\text{ref}} \) for efficient computation and apply Eq. (4) into the token-level alignment. Therefore, the TKPO loss can be derived by formulating a maximum likelihood objective, as follows,

\[
\mathcal{L}_{\text{TKPO}}(\pi_\theta) = -\mathbb{E}_{(x, y_{t_w}, \mathcal{Y}_t) \sim \mathcal{D}}
\log \frac{\exp\left(\pi_\theta(y_{t_w}|x)/\tau\right)}{\exp\left(\pi_\theta(y_{t_w}|x)/\tau\right) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp\left(\pi_\theta(y_{t_\ell}|x)/\tau\right)}
\] (6)

where \( y_{t_w} \) and \( \mathcal{Y}_{t_\ell} \) are the t-th generated accepted token and rejected token set, respectively. \( \pi_\theta(y_t|x) \) is the probability of t-th token \( y_t \), conditioned on the input \( x \) and the previously generated context \( y_{<t} \). \( \tau \) is a temperature ratio that controls the scaling of the preference difference. By comparing Eq. (6) and Eq. (5), we optimize the \( \mathcal{L}_{\text{TKPO}} \) to closely approximate the upper bounds of the optimal policy \( \pi^* \) under the Bradley-Terry framework.

We further integrated the self-supervised fine-tuning (SFT) and preference alignment into a single-step training process for efficiency (Hong et al., 2024), as below,

\[
\mathcal{L} = \mathcal{L}_{\text{SFT}} + \lambda \mathcal{L}_{\text{TKPO}}
\]
\[
= -\frac{1}{N} \sum_{i=1}^N \frac{1}{M} \sum_{t=1}^M \log \pi_\theta(y_{t_w}^i|x_i) +
\lambda \log \frac{\exp\left(\pi_\theta(y_{t_w}^i|x_i)/\tau\right)}{\exp\left(\pi_\theta(y_{t_w}^i|x_i)/\tau\right) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp\left(\pi_\theta(y_{t_\ell}^i|x_i)/\tau\right)}
\] (7)

where \( N \) is the number of samples, \( M \) is the length of output. \( \lambda \) is a hyperparameter that weights the loss between the self-supervised fine-tuning and preference alignment.

2.3 Attribute-driven Rejected Token Selection

As shown in Eq. (6), the rejected tokens are required for model training. Inspired by (Sun et al., 2023; Shao et al., 2024; Pei et al., 2023), the rejected token can be derived for self-alignment based on the fundamental capabilities of LLMs. Here, we propose a controllable attributes-driven method for rejected token selection. For the outline generation with the controllable attribute \( a \), e.g., literary language style, we denote the token \( y_t \) from the reference response as the accepted \( y_{t_w} \) and employ multinomial sampling under the anti-controllable attribute \( \bar{a} \) to derive \( K \) tokens as the rejected tokens (\( \{y_{t_\ell}\}_{\ell=1}^K \)), i.e., we sample \( K \) tokens based on the probabilities \( P_\theta(\hat{y}_t|x, \bar{a}, y_{<t}) \) generated by the prompts (ref. Appendix A for detail) under \( \bar{a} \) (e.g., objective language style).

\[
\begin{cases}
y_{t_w} = y_t \\
y_{t_\ell} \sim \text{Mult-Sampl}(P_\theta(\hat{y}_t|x, \bar{a}, y_{<t}))
\end{cases}
\] (8)

where \( y_{<t} \) is the first \( t-1 \) tokens from reference. Mult-Sampl(:) means multinomial sampling operation. Distinct from most preference alignment methods, it does not require response pairs for model training, alternatively, an attribute-driven method is applied for rejected sample construction. Therefore, we recognize TKPO as a self-alignment method. To sum up, the completed training process can be summarized in the Algorithm 1 (ref. Appendix C.2).

2.4 Properties of TKPO

Diversity and Hard Token Optimization. Intuitively, tokens associated with common expressions in everyday language are more likely to be
generated and are easier to optimize compared to tokens associated with domain-specific terminology. As a consequence, to enhance the diversity of the output, the objective function should, in turn, pay more attention to the defamiliarized and infrequent token optimization. Mathematically, we can derive the gradient of \( \mathcal{L}_{\text{TKPO}} \) with respect to the \( \pi_\theta(y_{t_w}|x) \) (ref. Appendix B.3 for detail) as follows,

\[
\frac{\partial}{\partial \pi_\theta(y_{t_w}|x)} \log \frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_e} \in \mathcal{Y}_{t_e}} \exp(\pi_\theta(y_{t_e}|x))}
= \frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_e} \in \mathcal{Y}_{t_e}} \exp(\pi_\theta(y_{t_e}|x))} - 1
\]

(9)

We then reformulate the summation over tokens \( y_{t_e} \) as an expectation, under the distribution \( p(y|x) \), where \( p(y|x) \) reveals the base model’s inherent token preference. Thus, we can obtain the gradient of \( \mathcal{L}_{\text{TKPO}} \) as:

\[
\frac{\exp(\pi_\theta(y_{t_w}|x))}{K \mathbb{E}_{y_{t_e} \sim p(y_{t_e}|x)} \exp(\pi_\theta(y_{t_e}|x))} - 1
\]

(10)

Here, we can see that the magnitude of gradient increases when \( \pi_\theta(y_{t_w}|x) \) is smaller (indicating a hard token to optimization) and \( \pi_\theta(y_{t_e}|x) \) is larger (corresponding to high-frequency tokens). This dynamic achieves two key properties: (1) focus on hard tokens optimization, i.e., the model prioritizes optimizing hard tokens as their lower likelihood amplifies their gradient magnitude during training; and (2) diversity enhancement, i.e., as rejected tokens (\( y_{t_e} \)) are sampled toward common high-probability tokens, in-turn, accepted tokens (\( y_{t_w} \)) are implicitly skewed toward less probable tokens, thereby, encouraging novel and diverse outputs.

3 Experiments

3.1 Datasets

We curate a total of 20,000 raw articles where 10,000 focused on language style and 10,000 on the level-of-detail from the WeChat Official Accounts Platform4, all published before December 2, 2024, for multi-style outline controllable generation. More concretely, the articles regarding the level-of-detail control are randomly selected and cover topics rooted in daily life. In contrast, the articles with respect to language style control are sourced from two magazines renowned for their in-depth analysis and professional viewpoints: Sanlian Lifeweek Magazine (“三联生活周刊”)5 and Newweekly Magazine (“新周刊”)6. These publications primarily focus on topics related to political commentary and literature reading, featuring outlines created by professional editors. The statistics of the two datasets are presented in Appendix F.1.

For the dataset with respect to the level-of-detail, we utilize GTP-4o (OpenAI, 2024) with prompts (ref. Appendix A) for reference outline generation. As for the dataset regarding the language style, we extract the raw outlines crafted by professional editors as the literary style reference output, while the outlines generated by GPT-4o are recognized as the objective style reference output. Therefore, we construct two datasets, respectively, for the level-of-detail and language style outline controllable generation. For each dataset, we randomly select 3% samples for testing, the remaining are used for training.

3.2 Evaluation Metrics

We consider BLEU (Papineni et al., 2002) and ROUGE (Lin and Hovy, 2003) to assess the overall generation quality, both of which consider the overlap of grams between generation (\( y_{gre} \)) and reference (\( y_{ref} \)) for evaluation and are widely adopted in generation tasks. As to measuring level-of-detail, the straightforward strategy to adopt the outline length and number of subheadings for performance evaluation might be inappropriate. For instance, it is meaningless to recognize a repeated and nonsense output as a high-quality comprehensive outline. Therefore, we calculate the similarity in outline length and number of subheadings using min-max operation between generation and reference to assess the performance.

\[
\begin{align*}
\text{WN}(y_{ref}, y_{gre}) &= \frac{\min(\#_w(y_{gre}), \#_w(y_{ref}))}{\max(\#_w(y_{gre}), \#_w(y_{ref}))} \\
\text{HN}(y_{ref}, y_{gre}) &= \frac{\min(\#_h(y_{gre}), \#_h(y_{ref}))}{\max(\#_h(y_{gre}), \#_h(y_{ref}))}
\end{align*}
\]

(11)

where \( \#_w(\cdot) \) and \( \#_h(\cdot) \) refer to the number of outline words and subheadings respectively.

4An online platform developed by Tencent Ltd. that allows businesses, organizations, and individuals to share regular information, express viewpoints, or provide entertainment and services such as e-commerce to the public. Users can also subscribe to official accounts to receive messages and access services. https://mp.weixin.qq.com/?token=&lang=en_US

5https://www.newweekly.com.cn/magazine

6https://www.newweekly.com.cn/
<table>
  <tr>
    <th>Dataset Metric</th>
    <th>DPO</th>
    <th>SimPO</th>
    <th>ORPO</th>
    <th>CPO</th>
    <th>TDPO</th>
    <th>FUDGE</th>
    <th>PREADD</th>
    <th>DExpert</th>
    <th>TKPO</th>
  </tr>
  <tr>
    <td rowspan="6">Level-of-detail</td>
    <td>ROUGE-1 ↑</td>
    <td>44.99</td>
    <td>45.09</td>
    <td>46.21</td>
    <td>45.17</td>
    <td>46.35</td>
    <td>45.35</td>
    <td>44.90</td>
    <td>45.88</td>
    <td><b>48.83</b></td>
  </tr>
  <tr>
    <td>ROUGE-2 ↑</td>
    <td>21.32</td>
    <td>22.18</td>
    <td>21.93</td>
    <td>21.63</td>
    <td>21.76</td>
    <td>21.63</td>
    <td>22.40</td>
    <td>22.39</td>
    <td><b>25.43</b></td>
  </tr>
  <tr>
    <td>ROUGE-L ↑</td>
    <td>32.94</td>
    <td>32.92</td>
    <td>33.88</td>
    <td>32.98</td>
    <td>34.04</td>
    <td>32.29</td>
    <td>32.74</td>
    <td>34.13</td>
    <td><b>36.78</b></td>
  </tr>
  <tr>
    <td>BLEU-4 ↑</td>
    <td>24.67</td>
    <td>24.55</td>
    <td>25.46</td>
    <td>24.90</td>
    <td>24.29</td>
    <td>24.50</td>
    <td>22.24</td>
    <td>25.58</td>
    <td><b>27.05</b></td>
  </tr>
  <tr>
    <td>WN ↑</td>
    <td>65.41</td>
    <td>63.81</td>
    <td>64.78</td>
    <td>65.48</td>
    <td>65.40</td>
    <td>65.75</td>
    <td>59.27</td>
    <td>64.20</td>
    <td><b>66.78</b></td>
  </tr>
  <tr>
    <td>HN ↑</td>
    <td>69.30</td>
    <td>69.65</td>
    <td>70.06</td>
    <td>66.67</td>
    <td>66.51</td>
    <td>68.40</td>
    <td>62.92</td>
    <td>67.99</td>
    <td><b>71.49</b></td>
  </tr>
  <tr>
    <td rowspan="6">Language Style</td>
    <td>ROUGE-1 ↑</td>
    <td>37.54</td>
    <td>37.34</td>
    <td>39.08</td>
    <td>41.18</td>
    <td>42.51</td>
    <td>37.80</td>
    <td>40.65</td>
    <td>41.39</td>
    <td><b>43.67</b></td>
  </tr>
  <tr>
    <td>ROUGE-2 ↑</td>
    <td>16.52</td>
    <td>17.83</td>
    <td>17.26</td>
    <td>19.30</td>
    <td>20.93</td>
    <td>17.45</td>
    <td>19.03</td>
    <td>19.49</td>
    <td><b>21.56</b></td>
  </tr>
  <tr>
    <td>ROUGE-L ↑</td>
    <td>28.41</td>
    <td>28.99</td>
    <td>29.05</td>
    <td>33.77</td>
    <td>32.00</td>
    <td>29.83</td>
    <td>31.05</td>
    <td>32.16</td>
    <td><b>34.04</b></td>
  </tr>
  <tr>
    <td>BLEU-4 ↑</td>
    <td>24.87</td>
    <td>22.52</td>
    <td>25.09</td>
    <td>28.42</td>
    <td>28.33</td>
    <td>25.96</td>
    <td>27.94</td>
    <td>27.99</td>
    <td><b>29.81</b></td>
  </tr>
  <tr>
    <td>OTE-O ↓</td>
    <td>2.10</td>
    <td>2.41</td>
    <td><b>1.72</b></td>
    <td>1.77</td>
    <td>2.25</td>
    <td>1.92</td>
    <td>1.94</td>
    <td>1.97</td>
    <td>1.75</td>
  </tr>
  <tr>
    <td>OTE-L ↑</td>
    <td>3.89</td>
    <td>3.86</td>
    <td>3.24</td>
    <td>3.52</td>
    <td>3.88</td>
    <td>4.08</td>
    <td>3.97</td>
    <td>4.03</td>
    <td><b>4.11</b></td>
  </tr>
</table>

Table 1: The overall performance (×100%) of TKPO and baselines. OTE-O and OTE-L are the token entropy of objective outlines and literary outlines, respectively. ↑ indicates that higher values are better, while ↓ indicates that lower values are better. The best results for each metric in each group are in bold.

For language style control, based on What is literariness? (Miall and Kuiken, 1999), literariness emerges when the language deviates from conventional usage, creating a sense of defamiliarization and foregrounding. Consequently, we can assess the literariness of the generation by observing the defamiliarization of word utilization. Owing to the paradigm of LLMs training, words that appear less frequently in the training corpus will inherently acquire lower probabilities during inference (Braysbaert et al., 2025), which, thereby, enhances the literariness and professionalism. Therefore, following (Zhang et al., 2024), we utilize the next-token likelihood and define the Outline Token Entropy (OTE) to measure the literariness:

\[
\text{OTE}(y) = - \frac{1}{M} \sum_{t=1}^{M} \log(\pi_{\text{ref}}(y_t|x))
\]

where \( M \) denotes the length of outputs \( y \), \( \pi_{\text{ref}} \) denotes the base model without training on the outline controllable generation datasets. As shown in Figure 8 and Table 4 in Appendix F.2, there is a substantial difference in the average OTE values between objective outlines (1.81) and literary outlines (4.01), indicating the effectiveness of this metric.

3.3 Baselines and Implementations

Baselines. We cover a broad range of baselines for evaluation: (1) preference alignment approaches, including DPO, SimPO, ORPO (Hong et al., 2024), CPO (Guo et al., 2024), and TDPO (Zeng et al., 2024); (2) decoding probability intervention methods, including FUDGE (Yang and Klein, 2021), PREADD (Pei et al., 2023), and DExpert (Liu et al., 2021). See Appendix D for detail.

Implementation Details. We perform multi-style outline controllable generation with a popular open-source LLM, Qwen2.5-3B (Bai et al., 2023), and further investigate the performance variance across different LLMs (up to 7B). All the experiments are conducted on 8 × NVIDIA V100 32G GPUs. The detailed hyperparameter settings are presented in Appendix E.

3.4 Overall Performance

Table 1 presents the overall performance of our TKPO against other methods on the language style and level-of-detail outline controllable generation. To sum up, owing to the inherent inconsistency in output styles between humans and LLMs, all the methods yield inferior results on language style controllable generation compared to the level-of-detail. Our method surpasses all the baselines for level-of-detail control. Regarding language style generation, our proposed TKPO outperforms all baseline models except ORPO on the OTE-O metric. However, ORPO exhibits the poorest performance on the OTE-L metric given the "alignment tax" (Ouyang et al., 2022), where enhancing alignment with objective outline generation compromises literary expressiveness. Figure 3 illustrates the detailed results of the concise, moderate, and comprehensive outline generation. Since the verbosity bias (Saito et al., 2023), i.e., LLMs in-
Figure 3: The overall performance for level-of-detail (concise vs. moderate vs. comprehensive) outlines controllable generation. Our TKPO method demonstrates superiority over all other baselines across all sub-datasets.

<table>
  <tr>
    <th>Method</th>
    <th>ROUGE-1</th>
    <th>ROUGE-2</th>
    <th>ROUGE-L</th>
    <th>BLEU-4</th>
    <th>OTE-O</th>
    <th>OTE-L</th>
  </tr>
  <tr>
    <td>Qwen2.5-3B Top-K Sampling</td>
    <td>43.14</td>
    <td>20.23</td>
    <td>33.14</td>
    <td>29.51</td>
    <td>1.86</td>
    <td>3.82</td>
  </tr>
  <tr>
    <td>Qwen2.5-3B w/o TKPO</td>
    <td>40.60</td>
    <td>19.46</td>
    <td>31.90</td>
    <td>28.22</td>
    <td>1.89</td>
    <td>3.58</td>
  </tr>
  <tr>
    <td>Qwen2.5-3B w/ TKPO</td>
    <td>43.67</td>
    <td>21.56</td>
    <td>34.04</td>
    <td>29.81</td>
    <td>1.75</td>
    <td>4.11</td>
  </tr>
  <tr>
    <td>Qwen2.5-0.5B w/ TKPO</td>
    <td>38.41</td>
    <td>17.52</td>
    <td>28.41</td>
    <td>24.49</td>
    <td>1.97</td>
    <td>3.52</td>
  </tr>
  <tr>
    <td>Qwen2.5-7B w/ TKPO</td>
    <td>44.53</td>
    <td>21.70</td>
    <td>35.04</td>
    <td>30.63</td>
    <td>1.88</td>
    <td>4.14</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B w/o TKPO</td>
    <td>40.24</td>
    <td>18.33</td>
    <td>29.60</td>
    <td>27.02</td>
    <td>3.75</td>
    <td>3.75</td>
  </tr>
  <tr>
    <td>LLaMA3.1-8B w/ TKPO</td>
    <td>42.51</td>
    <td>21.00</td>
    <td>32.94</td>
    <td>28.89</td>
    <td>1.91</td>
    <td>3.88</td>
  </tr>
</table>

Table 2: Ablation studies on language style dataset: (1) with (w/) and without (w/o) TKPO loss; (2) different LLM sizes and base models; (3) Top-K sampling vs. multinomial sampling in Eq. 6.

cline to output more verbose responses in the generation, all the baselines achieve comparable results in the moderate and the comprehensive controllable generation. However, for the concise control, our TKPO outperforms the best baseline by 12.39% on ROUGE-1 and 10.03% on BLEU-4. As for the language style control, the detailed results are reported in Appendix G.1. Overall, our method achieves the best results compared to the baseline models.

3.5 Ablation Studies

We further investigate the effect of different base LLMs, the size of LLMs, and rejected token sampling strategies (i.e., we apply Top-K sampling as an alternative to multinomial sampling) on the performance. As shown in Table 2, we could observe that the TKPO loss improves performance across various base models. Additionally, while increasing the model size generally enhances performance, the marginal gains diminish as the size continues to grow, e.g., 13.69% from Qwen2.5-0.5b to 3b vs. 1.97% from 3b to 7b in ROUGE-1. Furthermore, attributed to the more precise estimation of the optimal policy (Liu et al., 2024b), multinomial sampling achieves superior results compared to Top-K sampling. We could draw a similar conclusion on the level-of-detail dataset (ref. Table 5 in Appendix G.2).

3.6 In-depth Analysis

Training Efficiency. To verify the efficiency of our method against other baselines, we report the per-GPU peak memory usage and one-step training time7 of Qwen2.5-3B in Figure 4 (a). Since our TKPO does not utilize a reference model and involves only the accepted response during back-propagation in the training process, the GPU memory usage and runtime are significantly lower than those of the baselines. Specifically, our method reduces roughly 26% memory usage and 46% runtime, compared with DPO.

Hyperparameter Sensitive Analysis. We visual-

7The average time is calculated over 100 training steps over five random runs.
Figure 4: In-depth analysis on the level-of-detail dataset. (a) The per-GPU peak memory usage and one-step runtime. (b) Performance of TKDO across various \( \tau \) and \( K \) settings. A moderate value is preferred for achieving optimal results. (c) Negative logarithm JS divergence distribution, where \( p_\theta(y|\cdot) \) is the next-token predicted probability distribution, \( a \) and \( \bar{a} \) denote the controllable attribute (\emph{e.g.}, concise) and the anti-attribute (\emph{e.g.}, comprehensive). A smaller value indicates a greater difference in output probabilities under different controllable conditions. Our TKPO acquires a significant distinction compared to DPO and TDPO methods.

ize the performance of TKPO across various temperature ratios \( \tau \) and rejected token numbers \( K \) on the level-of-detail dataset, as shown in Figure 4 (b) (Full results are reported in Table 6). We recommend a moderate value, \emph{i.e.}, \( \tau = 0.15, K = 50 \) for outline level-of-detail controllable generation. However, for language style control, a larger value of \( K \) will facilitate the improvement of OTE, enhancing the literariness of the outlines (ref. Table 7 for detail). The results of various \( \lambda \) in Eq. 7 are presented in Table 8. Overall, a smaller value is recommended for a better performance.

JS Divergence. Given the controllable attribute \( a \) (\emph{e.g.}, concise) and anti-controllable attribute \( \bar{a} \) (\emph{e.g.}, comprehensive), we calculate the negative logarithm Jensen-Shannon (JS) divergence of next-token predicted probability distribution between them. Figure 4 (c) illustrates the frequency histogram of the test dataset in level-of-detail controllable outline generation. Our TKPO exhibits a more compact distribution that roughly ranges from 15 to 30 against TDPO and DPO methods. This suggests that TKPO possesses a superior ability to discern differences among various controllable conditions, thereby enabling higher-quality alignment in controllable generation.

4 Related Work

4.1 Controllable Generation

Overall, existing controllable generation attempts can be broadly categorized into three strategies: (1) probability intervention, (2) preference alignment, and (3) prompt engineering. PPLM (Dathathri et al., 2019) is a pioneer that manipulates the output probability for controllable generation. Building on this foundation, many works, including GeDi (Krause et al., 2021), FUDGE (Yang and Klein, 2021), and PREADD (Pei et al., 2023) have been proposed further. In contrast, another line of research within the probability intervention aims to reduce the probability of undesirable tokens while increasing the probability of preferred tokens for controllable generation, such as DExpert (Liu et al., 2021). Preference alignment is a general solution to align the LLMs with specific preferences or conditions, thus, the representative methods, such as DPO (Rafailov et al., 2024), SimPO (Meng et al., 2024), and ORPO (Hong et al., 2024), can also be applied for controllable generation. Building on these works, CPO (Guo et al., 2024) and DPA (Wang et al., 2024) further proposed to address multi-objective preference alignment controllable generation through data augmentation and rejection sampling. FIGA (Guo et al., 2023) and SparsePO (Christopoulou et al., 2024) employ the hard and soft token masks for finer-grained alignment. As for prompt engineering, Fonseca and Cohen (2024) introduces keywords in prompts for more precise control. Distinct from existing research, our method is response-pair-free, focusing on token-level preference alignment.

4.2 Outline Generation

Outline generation is a crucial task for text semantic structuralization, text understanding, and multiple downstream applications. However, few works put effort into this research avenue. Existing research on outline generation can be classified into two methodologies: (1) segmentation cascade summarization, and (2) prompt-driven methods. The first solution segments the article into dis-
tinct parts by detecting the section boundary (Sun et al., 2022; Zhang et al., 2019) or topic consistency (Jiang et al., 2024) across paragraphs. Afterward, the LLMs are applied to generate each segment’s headings or brief summarizations. On the contrary, the second category (Yang et al., 2023; Li et al., 2024) endeavors to devise tailored prompts for self-supervised fine-tuning, steering the LLMs to generate the outlines straightforwardly. Nevertheless, none of the studies focus on the multi-style outline controllable generation, thus, our work aims to fill this gap.

5 Conclusion

We introduce a multi-style outline controllable generation task and curate two datasets regarding the outline language style and level-of-detail. Additionally, a token-level preference self-alignment method, named TKPO, is proposed for a more efficient and precise controllable generation. Specifically, TKPO extends the Bradley-Terry model from pair-wise to list-wise comparison and further applies it to token-level alignment. TKPO does not require response-pair for model training, instead, a controllable attributes-driven self-alignment method is applied. Mathematical analysis demonstrates that TKPO confers benefits in output diversity and hard token optimization. Besides, optimizing TKPO is tantamount to optimizing the upper bound of the optimal policy. Compared to DPO, our TKPO achieves up to 19.28% performance improvement with only 73.55% memory usage and 56.25% training time.

Acknowledgment

This work was supported by National Natural Science Foundation of China (No. 62272349); Natural Science Foundation of Hubei Province under Grant Numbers 2023BAB160; Chenliang Li is the corresponding author.

Limitations

While conducting a comprehensive analysis of our TKPO against other preference alignment methods for outline controllable generation, we did not include multi-lingual outlines and other controllable generation tasks. We leave the broader range of comparison on other benchmarks as future work, along with scaling our method to over 7B models. Furthermore, we will refine the categorization of outline styles into finer-grained categories, engaging experts to compose multi-style outlines and perform evaluations, thereby verifying the effectiveness of our method comprehensively.

Ethics Considerations

As with any effective method for controllable generation and preference alignment, we acknowledge that TKPO could potentially be misused to amplify toxicity, gender bias, or other harmful attributes. Nonetheless, these methods, like ours, also serve as powerful tools for content moderation and mitigating harmful text generation.

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A  Prompt Design

We designed the following prompts for various stylized outline controllable generation when training the model.

A.1  Prompts for Level-of-detail Controllable Generation

<table>
  <tr>
    <th>Moderate</th>
  </tr>
  <tr>
    <td><b>Chinese:</b> 你是一个专业的杂志编辑。请对如下文章生成结构化、层次化目录。要求：真实客观。<br>\n 文章：<br><b>Translation:</b> You are a professional magazine editor. Please generate a structured and hierarchical outline for the following Article. Requirements: Accurate and Objective.<br>\nArticle:</td>
  </tr>
</table>

<table>
  <tr>
    <th>Concise</th>
  </tr>
  <tr>
    <td><b>Chinese:</b> 你是一个专业的杂志编辑。请对如下文章生成结构化、层次化目录。要求：简洁凝练。<br>\n 文章：<br><b>Translation:</b> You are a professional magazine editor. Please generate a structured and hierarchical outline for the following Article. Requirements: Concise and Succinct.<br>\nArticle:</td>
  </tr>
</table>

<table>
  <tr>
    <th>Comprehensive</th>
  </tr>
  <tr>
    <td><b>Chinese:</b> 你是一个专业的杂志编辑。请对如下文章生成结构化、层次化目录。要求：丰富全面。<br>\n 文章：<br><b>Translation:</b> You are a professional magazine editor. Please generate a structured and hierarchical outline for the following Article. Requirements: Comprehensive and Detailed.<br>\nArticle:</td>
  </tr>
</table>

A.2  Prompts for Language Style Controllable Generation

<table>
  <tr>
    <th>Objective</th>
  </tr>
  <tr>
    <td><b>Chinese:</b> 你是一个专业的杂志编辑。请对如下文章生成结构化、层次化目录。要求：真实客观。<br>\n 文章：<br><b>Translation:</b> You are a professional magazine editor. Please generate a structured and hierarchical outline for the following Article. Requirements: Accurate and Objective.<br>\nArticle:</td>
  </tr>
</table>

<table>
  <tr>
    <th>Literariness</th>
  </tr>
  <tr>
    <td><b>Chinese:</b> 你是一个专业的杂志编辑。请对如下文章生成结构化、层次化目录。要求：文学诗意。<br>\n 文章：<br><b>Translation:</b> You are a professional magazine editor. Please generate a structured and hierarchical outline for the following Article. Requirements: Literary and Poetic.<br>\nArticle:</td>
  </tr>
</table>
B Mathematical Derivations

B.1 Closed-Form of Reward Model

Building on prior work (Jaques et al., 2017), the preference alignment with reward learning aims to optimize the Eq. (13),

\[
\max_{\pi_\theta} \mathbb{E}_{x \sim D, y \sim \pi_\theta(y|x)}[r(x, y)] - \beta D_{\mathrm{KL}}[\pi_\theta(y|x)||\pi_{\mathrm{ref}}(y|x)]
\] (13)

where \( r \) is the reward model. \( \beta \) controls the deviation of the language model policy \( \pi_\theta \) from the base reference policy \( \pi_{\mathrm{ref}} \).

Following (Rafailov et al., 2024), the closed form of the reward model \( r(x, y) \) can be derived, we include it here for completeness:

\[
\begin{align*}
&\max_{\pi_\theta} \mathbb{E}_{x \sim D, y \sim \pi_\theta(y|x)}[r(x, y)] - \beta D_{\mathrm{KL}}[\pi_\theta(y|x)||\pi_{\mathrm{ref}}(y|x)] \\
&= \max_{\pi_\theta} \mathbb{E}_{x \sim D} \mathbb{E}_{y \sim \pi_\theta(y|x)} \left[ r(x, y) - \beta \log \frac{\pi_\theta(y|x)}{\pi_{\mathrm{ref}}(y|x)} \right] \\
&= \min_{\pi_\theta} \mathbb{E}_{x \sim D} \mathbb{E}_{y \sim \pi_\theta(y|x)} \left[ \log \frac{\pi_\theta(y|x)}{\pi_{\mathrm{ref}}(y|x)} - \frac{1}{\beta} r(x, y) \right] \\
&= \min_{\pi_\theta} \mathbb{E}_{x \sim D} \mathbb{E}_{y \sim \pi_\theta(y|x)} \left[ \log \frac{\pi_\theta(y|x)}{Z(x) \pi_{\mathrm{ref}}(y|x) \exp \left( \frac{1}{\beta} r(x, y) \right)} - \log Z(x) \right]
\end{align*}
\] (14)

Similar to the results proofed in (Peters et al., 2010; Peng et al., 2019), we can obtain an exact analytic solution,

\[
\pi_\theta(y|x) = \frac{1}{Z(x)} \pi_{\mathrm{ref}}(y|x) \exp \left( \frac{1}{\beta} r(x, y) \right)
\] (15)

where \( Z(x) = \sum_y \pi_{\mathrm{ref}}(y|x) \exp \left( \frac{1}{\beta} r(x, y) \right) \) is a partition function.

Despite the fact that the Eq (15) solution in itself is intractable, we can express the reward through its corresponding optimal policy \( \pi^*_\theta(y|x) \),

\[
r^*(x, y) = \beta \log \frac{\pi^*_\theta(y|x)}{\pi_{\mathrm{ref}}(y|x)} + \beta \log Z(x)
\] (16)

B.2 TKPO Loss Under the Bradley-Terry Framework

Considering the mutual independence of the probabilities associated with any two output tokens, we can extend the Bradley-Terry preference model from pair-wise to list-wise comparison. Therefore, we have

\[
p \left( y_w \succ \{y_\ell\}_{\ell=1}^K | x \right) = \prod_{\ell=1}^K p(y_w \succ y_\ell | x) = \prod_{\ell=1}^K \frac{\exp(r(y_w, x))}{\exp(r(y_w, x)) + \exp(r(y_\ell, x))}
\] (17)

Substituting Eq. (16) into Eq. (17) we have

\[
\begin{align*}
p^* \left( y_w \succ \{y_\ell\}_{\ell=1}^K | x \right) &= \prod_{\ell=1}^K \frac{\exp \left( \beta \log \frac{\pi^*_\theta(y_w|x)}{\pi_{\mathrm{ref}}(y_w|x)} + \beta \log Z(x) \right)}{\exp \left( \beta \log \frac{\pi^*_\theta(y_w|x)}{\pi_{\mathrm{ref}}(y_w|x)} + \beta \log Z(x) \right) + \exp \left( \beta \log \frac{\pi^*_\theta(y_\ell|x)}{\pi_{\mathrm{ref}}(y_\ell|x)} + \beta \log Z(x) \right)} \\
&= \prod_{\ell=1}^K \frac{\exp \left( \beta \log \frac{\pi^*_\theta(y_w|x)}{\pi_{\mathrm{ref}}(y_w|x)} \right)}{\exp \left( \beta \log \frac{\pi^*_\theta(y_w|x)}{\pi_{\mathrm{ref}}(y_w|x)} \right) + \exp \left( \beta \log \frac{\pi^*_\theta(y_\ell|x)}{\pi_{\mathrm{ref}}(y_\ell|x)} \right)}
\end{align*}
\] (18)
We further define \( \beta = 1 \). Given the fact that \( \prod_{i=1}^K (1 + x_i) > 1 + \sum_{i=1}^K x_i \), when \( x_i > 0 \). Therefore, we have,

\[
p^* \left( y_w \succ \{y_\ell\}_{\ell=1}^K | x \right) = \prod_{\ell=1}^K \frac{\pi_\theta^*(y_w|x)}{\pi_\text{ref}(y_w|x)} \cdot \frac{\pi_\theta^*(y_\ell|x)}{\pi_\text{ref}(y_\ell|x)}
\]
\[
\propto \prod_{\ell=1}^K \frac{\exp \left( \frac{\pi_\theta^*(y_w|x)}{\pi_\text{ref}(y_w|x)} \right)}{\exp \left( \frac{\pi_\theta^*(y_\ell|x)}{\pi_\text{ref}(y_\ell|x)} \right) + \exp \left( \frac{\pi_\theta^*(y_\ell|x)}{\pi_\text{ref}(y_\ell|x)} \right)}
\]
\[
= \prod_{\ell=1}^K \frac{1}{1 + \exp \left( \frac{\pi_\theta^*(y_\ell|x)}{\pi_\text{ref}(y_\ell|x)} - \frac{\pi_\theta^*(y_w|x)}{\pi_\text{ref}(y_w|x)} \right)}
\]
\[
< \frac{1}{1 + \sum_{\ell=1}^K \exp \left( \frac{\pi_\theta^*(y_\ell|x)}{\pi_\text{ref}(y_\ell|x)} - \frac{\pi_\theta^*(y_w|x)}{\pi_\text{ref}(y_w|x)} \right)}
\] (19)

Following (Meng et al., 2024; Yuan et al., 2024; Hong et al., 2024), we remove the reference model \( \pi_\text{ref} \) for efficient computation. Hence, we formulate a maximum likelihood objective for Eq. (19), from which the TKPO loss is derived as follows,

\[
\mathcal{L}_\text{TKPO}(\pi_\theta) = -\mathbb{E}_{(x, y_w, \{y_\ell\}_{\ell=1}^K) \sim \mathcal{D}} \log \frac{1}{1 + \sum_{\ell=1}^K \exp \left( \pi_\theta(y_\ell|x) - \pi_\theta(y_w|x) \right)}
\]
\[
= -\mathbb{E}_{(x, y_w, \{y_\ell\}_{\ell=1}^K) \sim \mathcal{D}} \log \frac{\exp \left( \pi_\theta(y_w|x) \right)}{\exp \left( \pi_\theta(y_w|x) \right) + \sum_{\ell=1}^K \exp \left( \pi_\theta(y_\ell|x) \right)}
\] (20)

By comparing Eq. (20) and Eq. (19), we can optimize the \( \mathcal{L}_\text{TKPO} \) to closely approximate optimize the upper bounds of the optimal policy model \( \pi^* \) under the Bradley-Terry framework.

B.3 Properties of TKPO Loss

The \( \mathcal{L}_\text{TKPO} \) is given by the Eq. (6), we start by deriving its gradient with regard to the \( \pi_\theta(y_{t_w}|x) \),

\[
\frac{\partial \mathcal{L}_\text{TKPO}}{\partial \pi_\theta(y_{t_w}|x)} = \frac{\partial}{\partial \pi_\theta(y_{t_w}|x)} \log \frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))}
\]
\[
= \frac{\partial}{\partial \pi_\theta(y_{t_w}|x)} \log \left( \frac{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))}{\exp(\pi_\theta(y_{t_w}|x))} \right)
\]
\[
= \frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))} \cdot \frac{\partial}{\partial \pi_\theta(y_{t_w}|x)} \frac{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))}{\exp(\pi_\theta(y_{t_w}|x))}
\]
\[
= \frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))} \cdot \frac{-\sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))}{\exp(\pi_\theta(y_{t_w}|x))^2} \cdot \exp(\pi_\theta(y_{t_w}|x))
\]
\[
= -\frac{\sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))}
\]
\[
= \frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + \sum_{y_{t_\ell} \in \mathcal{Y}_{t_\ell}} \exp(\pi_\theta(y_{t_\ell}|x))} - 1
\] (21)

Following the law of large numbers, we can reprint the sum operation of \( y_{t_\ell} \) with the expectation. Besides, given the fact that \( \exp(\pi_\theta(y|x)) \ll K \mathbb{E}_{y_{t_\ell} \sim p(y_{t_\ell}|x)} \exp(\pi_\theta(y_{t_\ell}|x)) \), when \( K \) is large enough, thus, the Eq. (21) can be written as:

\[
\frac{\exp(\pi_\theta(y_{t_w}|x))}{\exp(\pi_\theta(y_{t_w}|x)) + K \mathbb{E}_{y_{t_\ell} \sim p(y_{t_\ell}|x)} \exp(\pi_\theta(y_{t_\ell}|x))} - 1 \propto \frac{\exp(\pi_\theta(y_{t_w}|x))}{K \mathbb{E}_{y_{t_\ell} \sim p(y_{t_\ell}|x)} \exp(\pi_\theta(y_{t_\ell}|x))} - 1
\] (22)

This corresponds to Eq. 10 and subsequent analysis in the paper.
C  TKPO Algorithm

C.1  Pseudocode of TKPO Loss
PyTorch code for the TKPO loss is provided below:

import copy
import torch
from torch.nn import CrossEntropyLoss

def TKPO_loss(logits, logits_anti, labels, tau, k, lambdaa):
    """
    logits: logits with the controllable attribute, Shape: (batch_size, sequence_length, vocab_size);
    logits_anti: logits with anti-controllable attribute, Shape: (batch_size, sequence_length, vocab_size);
    labels: labels, Shape: (batch_size, sequence_length);
    tau: temperature ratio controlling the strength of preference difference;
    k: number of rejected tokens;
    lambdaa: the weight factor adjusts the influence weight of CE loss and TKPO loss;
    """
    # ============================= TKPO loss =============================
    # Shift so that tokens < n predict n
    shift_logits = logits[...,:-1,:].contiguous()
    pos_token = labels[..., 1:].contiguous()
    probs = torch.softmax(shift_logits, dim=-1)
    pos_tokencp = copy.deepcopy(pos_token)
    pos_tokencp[pos_token==100] = 100
    pos_prob = torch.gather(probs, 2, pos_tokencp.unsqueeze(-1).unsqueeze(0)).squeeze(-1)
    pos_prob = torch.exp(pos_prob/tau)

    logits_anti = logits_anti.detach()
    logits_anti.scatter_(2, pos_tokencp.unsqueeze(-1).unsqueeze(0), -1e6)
    # _, neg_token = torch.topk(logits_anti, k, dim=-1) # Top-K Sampling
    anti_probs = torch.softmax(logits_anti, dim=-1).squeeze(0)
    neg_token=torch.multinomial(anti_probs,k,replacement=False).unsqueeze(0) # Multinomial Sampling
    neg_probs = torch.gather(probs, 2, neg_token)
    neg_probs = torch.sum(torch.exp(neg_probs/tau), dim=-1)
    loss_tkpo = pos_prob/(pos_prob + neg_probs)
    mask_label = torch.ones_like(pos_token)
    mask_label[pos_token==100] = 0
    loss_tkpo = -torch.log(torch.sum((loss_tkpo*mask_label),dim=-1)/torch.sum(mask_label,dim=-1))

    # ============================= CE loss =============================
    # Flatten the tokens
    loss_fct_ce = CrossEntropyLoss()
    shift_logits = shift_logits.view(-1, logits.shape[-1])
    shift_labels = pos_token.view(-1)
    # Enable model parallelism
    shift_labels = shift_labels.to(shift_logits.device)
    loss_ce = loss_fct_ce(shift_logits, shift_labels)

    # ============================= loss =============================
    loss = loss_ce + lambdaa * loss_tkpo
    return loss
Algorithm 1: Token-level Preference Alignment (TKPO)

1: Input:
2: Controllable attributes: \( a \in \mathcal{A} \), Datasets with different \( a \): \( \mathcal{D} = \{(y, x, a)_i\}_{i=1}^N \);
3: Learning rate: \( \eta \), Training epoch: \( E \), Rejected token numbers: \( K \);
4: Policy model: \( \pi_\theta \), Temperature ratio: \( \tau \), Coefficient: \( \lambda \);
5: for each epoch in \( E \) do
6: Sample mini-batch \( \mathcal{D}_m = \{(y, x, a, \bar{a})_i\}_{i=1}^m \) from \( \mathcal{D} \);
7: Rejected tokens sampling: \( \{y_{t,\ell_i}\}_{i=1}^K \sim \text{Mult-Sampling}(P_\theta(\hat{y}_t|x, \bar{a}, y_{<t})) \);
8: \( \theta \leftarrow \theta + \eta \; \nabla_\theta \mathbb{E}_{(y,x,a,\bar{a}) \sim \mathcal{D}_m} \left[ \log \pi_\theta(y_{t,w}|x, a) + \lambda \log \frac{\exp(\pi_\theta(y_{t,w}|x,a)/\tau)}{\exp(\pi_\theta(y_{t,w}|x,a)/\tau) + \sum_{y_{t,\ell} \in \mathcal{Y}_{t,\ell}} \exp(\pi_\theta(y_{t,\ell}|x,\bar{a})/\tau)} \right] \);
9: end for
10: Output:
11: Updated policy model: \( \pi_\theta \);

C.2 Algorithm of TKPO Loss

The training process of TKPO is shown in Algorithm 1.

D Baselines

We select eight controllable generation baseline models, covering two research lines: (1) preference alignment and (2) decoding probability intervention, for performance evaluation. All of them are highly related to our research.

(1) Preference Alignment Methods:

• DPO (Rafailov et al., 2024) simplifies the RLHF by introducing the closed-form expression of the optimal reward model into the Bradley-Terry pair-wise ranking framework, bypassing the explicit learning requirements of the reward model.

• SimPO (Meng et al., 2024) further simplifies the DPO by using the average log probability of a sequence as the implicit reward, thus, obviating the necessity for a reference model.

• ORPO (Hong et al., 2024) integrates the SFT and preference alignment possesses into one stage, and then, calculates the odds ratio for favored and disfavored generations contrasting. It also eliminates the reference model for computation efficiency.

• CPO (Guo et al., 2024) develops DPO to the multi-objective alignment through controllable attribute prompt-driven and data augmentation methods.

• TDPO (Zeng et al., 2024) applies DPO at the token level, improving the alignment and diversity of the outputs.

(2) Decoding Probability Intervention Methods:

• FUDGE (Yang and Klein, 2021) factorizes the controllable generate probability via Bayesian rule. After that, the conditional attributes probability and the next token probability are modeled separately for controllable generation.

• PREADD (Pei et al., 2023) considers both the original outputs as well as the outputs from the prefix-prepended prompts when decoding for controllable generation.

• DExpert (Liu et al., 2021) aims to increase the probability of preferred tokens yield by the secure model while simultaneously reducing the probability of undesired tokens from the toxic model for controllable generation.

The objection functions of the baselines can be summarized in Table 3.
<table>
  <tr>
    <th>Baselines</th>
    <th>Objective</th>
    <th>Hyperparameter</th>
  </tr>
  <tr>
    <td>DPO</td>
    <td>\( -\mathbb{E}_{(x, y_w, y_t) \sim \mathcal{D}} \left[ \log \sigma \left( \beta \log \frac{\pi_\theta(y_w|x)}{\pi_{\text{ref}}(y_w|x)} - \beta \log \frac{\pi_\theta(y_t|x)}{\pi_{\text{ref}}(y_t|x)} \right) \right] \)</td>
    <td>\( \beta \in [0.01, 0.05, 0.1] \)</td>
  </tr>
  <tr>
    <td>SimPO</td>
    <td>\( -\mathbb{E}_{(x, y_w, y_t) \sim \mathcal{D}} \left[ \log \sigma \left( \frac{\beta}{|y_w|} \log \pi_\theta(y_w|x) - \frac{\beta}{|y_t|} \log \pi_\theta(y_t|x) - \gamma \right) \right] \)</td>
    <td>\( \beta \in [2.0, 2.5], \gamma \in [0.5, 1.0, 1.2, 1.4] \)</td>
  </tr>
  <tr>
    <td>ORPO</td>
    <td>\( -\mathbb{E}_{(x, y_w, y_t) \sim \mathcal{D}} \left[ \log p_\theta(y_w|x) + \lambda \log \sigma \left( \log \frac{p_\theta(y_w|x)}{1-p_\theta(y_w|x)} - \log \frac{p_\theta(y_t|x)}{1-p_\theta(y_t|x)} \right) \right], \)<br>where \( p_\theta(y|x) = \exp \left( \frac{1}{|y|} \pi_\theta(y|x) \right) \)</td>
    <td>\( \lambda \in [0.1, 0.5, 1.0, 2.0] \)</td>
  </tr>
  <tr>
    <td>CPO</td>
    <td>\( -\mathbb{E}_{(x, a, y_w, y_t) \sim \mathcal{D}} \left[ \log \sigma \left( \beta \log \frac{\pi_\theta(y_w|a,x)}{\pi_{\text{ref}}(y_w|a,x)} - \beta \log \frac{\pi_\theta(y_t|a,x)}{\pi_{\text{ref}}(y_t|a,x)} \right) \right] \)</td>
    <td>\( \lambda \in [0.01, 0.05, 0.1] \)</td>
  </tr>
  <tr>
    <td></td>
    <td>\( -\mathbb{E}_{(x, y_w, y_t) \sim \mathcal{D}} \left[ \log \sigma \left( \left( \beta \log \frac{\pi_\theta(y_w|x)}{\pi_{\text{ref}}(y_w|x)} - \beta \log \frac{\pi_\theta(y_t|x)}{\pi_{\text{ref}}(y_t|x)} \right) \right) \right] \)</td>
    <td>\( \beta \in [0.01, 0.05, 0.1] \)</td>
  </tr>
  <tr>
    <td>TDPO</td>
    <td>\( - (\beta D_{\text{SeqKL}}(x, y_t; \pi_{\text{ref}} \| \pi_\theta) - \beta D_{\text{SeqKL}}(x, y_w; \pi_{\text{ref}} \| \pi_\theta)) \),<br>where \( D_{\text{SeqKL}}(x, y; \pi_1 \| \pi_2) = \sum_{t=1}^T D_{\text{KL}}(\pi_1(\cdot|x, y^{<t>}) \| \pi_2(\cdot|x, y^{<t>})) \)</td>
    <td></td>
  </tr>
  <tr>
    <td>FUDGE</td>
    <td>\( -\mathbb{E}_{(x, y) \sim \mathcal{D}} [\log p(y|x)p(a|y, x)] \)</td>
    <td></td>
  </tr>
  <tr>
    <td>PREADD</td>
    <td>\( -\mathbb{E}_{(x, y) \sim \mathcal{D}} [\log p(y|x)^{1-\alpha} p(a|y, x)^{\alpha}] \)</td>
    <td>\( \alpha \in [-5, -2, 0, 2, 5] \)</td>
  </tr>
  <tr>
    <td>DExpert</td>
    <td>\( -\mathbb{E}_{(x, y) \sim \mathcal{D}} [\log \operatorname{softmax}(z(y|x) + \alpha (z^+(y|x) - z^-(y|x))] \)</td>
    <td>\( \alpha \in [1.0, 2.2] \)</td>
  </tr>
</table>

Table 3: Objection functions and hyperparameter search ranges for the compared baseline models. Given preference data \( \mathcal{D}(x, a, y_w, y_t) \), where \( x \) is the input, \( a \) denotes the controllable attributes, \( y_w \) and \( y_t \) are the accepted and rejected responses. \( \pi_\theta \) and \( \pi_{\text{ref}} \) correspond to the language model policy and reference policy, respectively. \( z, z^+ \), and \( z^- \) are the logits yielded by the base, secure, and toxic models, respectively.

E Implementation Details

To ensure a fair comparison, we conduct thorough hyperparameter tuning for all methods compared in our experiments. Specifically, we preliminary set the learning rate to \( 1e-5 \), utilizing a cosine learning rate schedule with a warm-up phase accounting for 10% of the steps over one epoch, within a total of seven epochs. In addition, we individually search the optimal learning rates in the range of \([5e-7, 1e-6, 5e-6, 5e-5, 1e-5]\) for each baseline model. We set the batch size as 1. The temperature ratio \( \tau \), and rejected token numbers \( K \) are searched within the range of \([0.1, 0.15, 0.2]\) and \([5, 20, 50, 100, 500]\), respectively. The hyperparameter \( \lambda \) in Eq. (7) is searched within the range of \([0.1, 0.5, 1.0]\). Moreover, all the models are fine-tuned with the Adam (Kingma, 2014) optimization method and all the experiments are conducted on 8× NVIDIA Tesla V100 32G GPUs. The key hyperparameter search ranges of other baselines are presented in Table 3.

F Dataset Statistics

F.1 Raw Article Statistics

We randomly select 10,000 articles published before Sep. 10, 2024 from WeChat Official Accounts Platform\(^8\). Using GPT-4o with tailored prompts (ref. Appendix A.1 for detail) to obtain three categories of outlines, i.e., concise vs. moderate vs. comprehensive (named C²O dataset), for outline level-of-detail controllable generation. Moreover, we collect 10,000 articles from Sanlian Lifeweek Magazine ("三联生活周刊"\(^9\)) and Newweekly Magazine ("新周刊"\(^10\)) published before Dec. 2, 2024. The articles from these two official accounts are known for their profound yet sharp insights. Considering the raw article outlines are written by professional editors, exhibiting rich literary and artistic characteristics, we refer to them as literary outlines, while the outlines generated by GPT-4o with prompts (ref. Appendix A.2 for detail) are recognized as objective outlines. Therefore, we curate a dataset, denoted as LO\(^2\), for the language style controllable generation. For all datasets, any information that names or uniquely identifies individual people has been removed.

We report the distribution of article categories

\(^8\)An online platform developed by Tencent Ltd. that allows businesses, organizations, and individuals to share regular information, express viewpoints, or provide entertainment and services such as e-commerce to the public. Users can also subscribe to official accounts to receive messages and access services. https://mp.weixin.qq.com/?token=&lang=en_US
\(^9\)https://www.newweekly.com.cn/magazine
\(^10\)https://www.newweekly.com.cn/
Figure 5: Statistical distribution of the C^2O (concise vs. moderate vs. comprehensive) dataset: the left subfigure manifests the article category distribution, while the right subfigure presents the distribution of article word numbers.

Figure 6: Statistical distribution of the LO^2 dataset: the left subfigure manifests the article category distribution, while the right subfigure presents the article word numbers distribution.

and word counts for these two datasets, as shown in Figure 5 and Figure 6, respectively. It is evident that a significant portion of the articles in the LO^2 dataset are concentrated in the literary criticism, and are consequently classified under the literature and reading category. In contrast, the articles from the C^2O dataset are mostly classified under society and livelihood. Moreover, the average article length in the LO^2 dataset (3,705.17 words) is significantly longer than that in the C^2O dataset (1,928.60 words), indicating a higher degree of professionalism and literacy.

F.2 Outline Statistics

Level-of-detail. For the level-of-detail dataset, we analyze the outline word count, number of headings, and word count per heading, as illustrated in Figure 7. Comparing the statistical distribution of concise, moderate, and comprehensive outlines, the average outline lengths are 58.24 words, 250.42 words, and 893.46 words, respectively. That is, the average lengths and the average number of headings in comprehensive outlines are approximately fifteen and nine times greater than those in concise outlines, highlighting a significant disparity in outline structure.

Language Style. Following Eq (12), we randomly select 3,000 literary and objective outlines, investigating their average token entropy distribution, as shown in Figure 8. It can be observed that over 70% literary outlines exhibit an average token entropy within the interval of 3 to 5, while this metric decreases to a range of 1 to 2 for the objective outlines. This indicates the distinct differences in vocabulary usage and language defamiliarization between literary and objective styles.
Figure 7: Statistical distributions (outline word count, number of headings, and word count per heading) of concise (top), moderate (middle), and comprehensive (bottom) outlines.

<table>
  <tr>
    <th colspan="2">Outline: Level-of-detail</th>
    <th colspan="2">Outline: Language Styles</th>
  </tr>
  <tr>
    <th># Words</th>
    <th># Headings</th>
    <th># Headings</th>
    <th>OTE</th>
  </tr>
  <tr>
    <td>Concise</td>
    <td>58.24</td>
    <td>5.68</td>
    <td></td>
  </tr>
  <tr>
    <td>Moderate</td>
    <td>250.42</td>
    <td>22.06</td>
    <td></td>
  </tr>
  <tr>
    <td>Comprehensive</td>
    <td>896.46</td>
    <td>51.06</td>
    <td></td>
  </tr>
  <tr>
    <td>Objective</td>
    <td>297.80</td>
    <td>1.81</td>
    <td></td>
  </tr>
  <tr>
    <td>Literary</td>
    <td>71.32</td>
    <td>4.01</td>
    <td></td>
  </tr>
</table>

Table 4: The statistics of outlines. # indicates count number operation. OTE refers to the outline token entropy, defined in Eq 12.

Overall. The average number of words, the average number of headings, and the average outline token entropy are shown in Table 4.
Figure 8: The average outline token entropy (OTE, defined in Eq 12) distribution with regard to literary (right) and objective (left) outlines.

G  Experimental Details

G.1  Language Style Dataset Performance
The performance of language style outline controllable generation (literary vs. objective) is shown in Figure 9. Our solution is superior to all the baseline models across all the sub-datasets.

G.2  Ablation Study of Level-of-detail Dataset
The experimental results of the ablation study with various model sizes (0.5B vs. 3B vs. 7B), base models (Qwen2.5 vs. LLaMA3.1), and sampling strategies (multinomial sampling vs. Top-K sampling) on level-of-detail outline controllable generation, as shown in Table 5. We can find that (1) the multinomial sampling outperforms Top-K sampling, and (2) TKPO loss can enhance performance across various base models and model sizes consistently and significantly.

G.3  Hyperparameter Sensitive Analysis
The impact of hyperparameter temperature ratio \( \tau \) and rejected token numbers \( K \) in Eq. 7 are shown in Table 6 (level-of-detail dataset) and Table 7 (language style dataset). The TKPO loss curve across various \( \tau \) is illustrated in Figure 10. Increasing the value of \( \tau \), the \( \mathcal{L}_{TKPO} \) decrease simultaneously. However, this does not mean larger \( \tau \) is always better for performance improvement. In general, we recommend a moderate value, e.g., \( \tau = 0.15 \) and \( K = 50 \) for the optimal performance. Additionally, the results with various \( \lambda \) settings in Eq 7 are reported in Table 8. We could observe that a smaller value is recommended for better performance.

G.4  Output Probability Difference
We further observe the probability differences between the accepted token and the rejected tokens (\( i.e. \), \( p(y_t|x) - \frac{1}{K} \sum_{i=1}^{K} p(y_{t_i}|x) \), where \( K \) is the number of sampling rejected tokens) during the TKPO and DPO training in language style dataset, as shown in Figure 11. It illustrates the probability discrimination between the accepted token and the rejected tokens in our TKPO enlarges as the training proceeds, whereas the DPO difficult to model the preference comparison at token levels, thereby failing to widen the margin between them.
Figure 9: The overall performance of language style (objective vs. literary) outlines controllable generation. We multiply the OTE metric by 10 for readability, denoted as OTE \( \times 10 \). Our TKPO method is superior to the other baselines on both literary and objective datasets.

<table>
  <tr>
    <th>Method</th>
    <th>ROUGE-1</th>
    <th>ROUGE-2</th>
    <th>ROUGE-L</th>
    <th>BLEU-4</th>
    <th>WN</th>
    <th>HN</th>
  </tr>
  <tr>
    <td><b>Qwen2.5-3B Top-K Sampling</b></td>
    <td>48.18</td>
    <td>24.48</td>
    <td>35.68</td>
    <td>27.85</td>
    <td>65.88</td>
    <td>68.79</td>
  </tr>
  <tr>
    <td><b>Qwen2.5-3B w/o TKPO</b></td>
    <td>46.62</td>
    <td>22.58</td>
    <td>33.57</td>
    <td>25.91</td>
    <td>65.13</td>
    <td>67.40</td>
  </tr>
  <tr>
    <td><b>Qwen2.5-3B w/ TKPO</b></td>
    <td>48.83</td>
    <td>25.43</td>
    <td>36.78</td>
    <td>27.05</td>
    <td>66.78</td>
    <td>71.49</td>
  </tr>
  <tr>
    <td><b>Qwen2.5-0.5B w/ TKPO</b></td>
    <td>40.59</td>
    <td>19.69</td>
    <td>28.59</td>
    <td>21.44</td>
    <td>55.70</td>
    <td>58.60</td>
  </tr>
  <tr>
    <td><b>Qwen2.5-7B w/ TKPO</b></td>
    <td>49.92</td>
    <td>26.05</td>
    <td>37.29</td>
    <td>28.17</td>
    <td>66.20</td>
    <td>71.56</td>
  </tr>
  <tr>
    <td><b>LLaMA3.1-8B w/o TKPO</b></td>
    <td>45.59</td>
    <td>22.53</td>
    <td>31.99</td>
    <td>23.04</td>
    <td>63.35</td>
    <td>66.11</td>
  </tr>
  <tr>
    <td><b>LLaMA3.1-8B w TKPO</b></td>
    <td>47.61</td>
    <td>22.79</td>
    <td>34.20</td>
    <td>26.62</td>
    <td>66.90</td>
    <td>68.14</td>
  </tr>
</table>

Table 5: Ablation studies on the level-of-detail dataset: (1) with (w/) and without (w/o) TKPO loss; (2) different LLM sizes and base models; (3) Top-K sampling vs. multinomial sampling in Eq. 6.

<table>
  <tr>
    <th>\( \tau \)</th>
    <th>K</th>
    <th>ROUGE-1</th>
    <th>ROUGE-2</th>
    <th>ROUGE-L</th>
    <th>BLEU-4</th>
    <th>WN</th>
    <th>HN</th>
  </tr>
  <tr>
    <td rowspan="5"><b>0.10</b></td>
    <td>5</td>
    <td>47.41</td>
    <td>25.16</td>
    <td>35.50</td>
    <td>27.67</td>
    <td>66.51</td>
    <td>69.17</td>
  </tr>
  <tr>
    <td>20</td>
    <td>46.35</td>
    <td>21.76</td>
    <td>34.04</td>
    <td>24.29</td>
    <td>65.40</td>
    <td>66.51</td>
  </tr>
  <tr>
    <td>50</td>
    <td>48.92</td>
    <td>23.98</td>
    <td>36.31</td>
    <td>27.56</td>
    <td>69.40</td>
    <td>70.16</td>
  </tr>
  <tr>
    <td>100</td>
    <td>48.42</td>
    <td>23.08</td>
    <td>34.53</td>
    <td>27.74</td>
    <td>69.85</td>
    <td>68.50</td>
  </tr>
  <tr>
    <td>500</td>
    <td>46.53</td>
    <td>21.98</td>
    <td>33.66</td>
    <td>25.09</td>
    <td>65.82</td>
    <td>69.37</td>
  </tr>
  <tr>
    <td rowspan="5"><b>0.15</b></td>
    <td>5</td>
    <td>47.92</td>
    <td>24.11</td>
    <td>35.54</td>
    <td>27.24</td>
    <td>62.84</td>
    <td>66.61</td>
  </tr>
  <tr>
    <td>20</td>
    <td>48.91</td>
    <td>24.55</td>
    <td>35.27</td>
    <td>27.24</td>
    <td>65.80</td>
    <td>64.64</td>
  </tr>
  <tr>
    <td>50</td>
    <td>50.08</td>
    <td>27.20</td>
    <td>38.59</td>
    <td>28.38</td>
    <td>68.92</td>
    <td>72.54</td>
  </tr>
  <tr>
    <td>100</td>
    <td>48.54</td>
    <td>25.08</td>
    <td>36.29</td>
    <td>26.22</td>
    <td>63.09</td>
    <td>67.54</td>
  </tr>
  <tr>
    <td>500</td>
    <td>47.65</td>
    <td>24.24</td>
    <td>36.04</td>
    <td>26.89</td>
    <td>63.72</td>
    <td>67.21</td>
  </tr>
  <tr>
    <td rowspan="5"><b>0.20</b></td>
    <td>5</td>
    <td>48.80</td>
    <td>25.05</td>
    <td>37.15</td>
    <td>28.32</td>
    <td>68.73</td>
    <td>68.74</td>
  </tr>
  <tr>
    <td>20</td>
    <td>49.05</td>
    <td>24.60</td>
    <td>36.81</td>
    <td>28.79</td>
    <td>67.47</td>
    <td>69.24</td>
  </tr>
  <tr>
    <td>50</td>
    <td>48.84</td>
    <td>25.43</td>
    <td>36.78</td>
    <td>27.05</td>
    <td>65.44</td>
    <td>69.84</td>
  </tr>
  <tr>
    <td>100</td>
    <td>46.69</td>
    <td>23.76</td>
    <td>34.58</td>
    <td>23.39</td>
    <td>61.18</td>
    <td>66.45</td>
  </tr>
  <tr>
    <td>500</td>
    <td>47.10</td>
    <td>22.97</td>
    <td>34.27</td>
    <td>26.15</td>
    <td>66.01</td>
    <td>66.88</td>
  </tr>
</table>

Table 6: Performance of TKDO across various \( \tau \) and \( K \) settings on the level-of-detail dataset.
<table>
  <tr>
    <th>τ</th>
    <th>K</th>
    <th>ROUGE-1</th>
    <th>ROUGE-2</th>
    <th>ROUGE-L</th>
    <th>BLEU-4</th>
    <th>OTE-L</th>
    <th>OTE-O</th>
  </tr>
  <tr>
    <td rowspan="5">0.10</td>
    <td>5</td>
    <td>43.95</td>
    <td>22.25</td>
    <td>34.98</td>
    <td>30.88</td>
    <td>3.87</td>
    <td>1.91</td>
  </tr>
  <tr>
    <td>20</td>
    <td>44.71</td>
    <td>21.96</td>
    <td>35.47</td>
    <td>32.08</td>
    <td>3.68</td>
    <td>1.98</td>
  </tr>
  <tr>
    <td>50</td>
    <td>43.99</td>
    <td>21.89</td>
    <td>34.55</td>
    <td>30.67</td>
    <td>3.66</td>
    <td>1.85</td>
  </tr>
  <tr>
    <td>100</td>
    <td>43.13</td>
    <td>21.24</td>
    <td>33.82</td>
    <td>30.37</td>
    <td>3.98</td>
    <td>1.77</td>
  </tr>
  <tr>
    <td>500</td>
    <td>43.11</td>
    <td>21.20</td>
    <td>33.63</td>
    <td>29.71</td>
    <td>3.82</td>
    <td>1.86</td>
  </tr>
  <tr>
    <td rowspan="5">0.15</td>
    <td>5</td>
    <td>43.39</td>
    <td>21.08</td>
    <td>33.85</td>
    <td>30.31</td>
    <td>3.67</td>
    <td>1.99</td>
  </tr>
  <tr>
    <td>20</td>
    <td>44.14</td>
    <td>21.65</td>
    <td>34.17</td>
    <td>30.68</td>
    <td>3.61</td>
    <td>1.81</td>
  </tr>
  <tr>
    <td>50</td>
    <td>43.69</td>
    <td>21.56</td>
    <td>34.87</td>
    <td>30.79</td>
    <td>3.88</td>
    <td>1.83</td>
  </tr>
  <tr>
    <td>100</td>
    <td>43.01</td>
    <td>21.59</td>
    <td>34.57</td>
    <td>30.72</td>
    <td>4.13</td>
    <td>1.90</td>
  </tr>
  <tr>
    <td>500</td>
    <td>43.40</td>
    <td>21.12</td>
    <td>34.21</td>
    <td>29.68</td>
    <td>4.07</td>
    <td>1.75</td>
  </tr>
  <tr>
    <td rowspan="5">0.20</td>
    <td>5</td>
    <td>43.42</td>
    <td>21.74</td>
    <td>34.45</td>
    <td>30.78</td>
    <td>3.83</td>
    <td>2.01</td>
  </tr>
  <tr>
    <td>10</td>
    <td>43.16</td>
    <td>22.17</td>
    <td>34.10</td>
    <td>29.75</td>
    <td>3.98</td>
    <td>1.92</td>
  </tr>
  <tr>
    <td>50</td>
    <td>43.02</td>
    <td>21.37</td>
    <td>34.44</td>
    <td>30.44</td>
    <td>3.57</td>
    <td>1.82</td>
  </tr>
  <tr>
    <td>100</td>
    <td>42.51</td>
    <td>20.61</td>
    <td>32.78</td>
    <td>28.67</td>
    <td>4.12</td>
    <td>1.90</td>
  </tr>
  <tr>
    <td>500</td>
    <td>42.04</td>
    <td>19.82</td>
    <td>32.67</td>
    <td>27.44</td>
    <td>4.18</td>
    <td>1.92</td>
  </tr>
</table>

Table 7: Performance of TKDO across various \( \tau \) and \( K \) settings on the language style dataset.

<table>
  <tr>
    <th>Dataset \( \lambda \)</th>
    <th>ROUGE-1</th>
    <th>ROUGE-2</th>
    <th>ROUGE-L</th>
    <th>BLEU-4</th>
    <th>WN</th>
    <th>HN</th>
    <th>OTE-O</th>
    <th>OTE-L</th>
  </tr>
  <tr>
    <td rowspan="3">C<sup>2</sup>O</td>
    <td>0.1</td>
    <td>48.10</td>
    <td>25.06</td>
    <td>35.82</td>
    <td>25.40</td>
    <td>65.19</td>
    <td>71.31</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>0.5</td>
    <td>48.83</td>
    <td>25.43</td>
    <td>36.78</td>
    <td>27.05</td>
    <td>66.78</td>
    <td>71.49</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>1.0</td>
    <td>47.48</td>
    <td>23.17</td>
    <td>34.85</td>
    <td>26.60</td>
    <td>62.99</td>
    <td>66.50</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td rowspan="3">LO<sup>2</sup></td>
    <td>0.1</td>
    <td>43.67</td>
    <td>21.56</td>
    <td>34.04</td>
    <td>29.81</td>
    <td>-</td>
    <td>-</td>
    <td>1.75</td>
    <td>4.11</td>
  </tr>
  <tr>
    <td>0.5</td>
    <td>42.36</td>
    <td>20.61</td>
    <td>32.75</td>
    <td>29.73</td>
    <td>-</td>
    <td>-</td>
    <td>1.88</td>
    <td>3.94</td>
  </tr>
  <tr>
    <td>1.0</td>
    <td>41.56</td>
    <td>20.48</td>
    <td>32.83</td>
    <td>29.37</td>
    <td>-</td>
    <td>-</td>
    <td>1.90</td>
    <td>4.03</td>
  </tr>
</table>

Table 8: Performance of TKPO across various \( \lambda \) in Eq. 7. C<sup>2</sup>O and LO<sup>2</sup> denote the level-of-detail dataset and language style dataset, respectively.

![TKPO loss curve across various values of τ on the language style dataset](page_101_1012_495_312.png)

Figure 10: The TKPO loss curve across various values of \( \tau \) on the language style dataset. The loss consistently decreases during training. With a larger \( \tau \) (e.g., \( \tau = 0.2 \)), \( \mathcal{L}_{TKPO} \) obtains a smaller value in training the models, indicating a more pronounced discrimination in the generation probability between accepted token and rejected tokens.

![Probability difference between the accepted token and the rejected tokens on language style dataset](page_671_1012_495_312.png)

Figure 11: Probability difference between the accepted token and the rejected tokens on language style dataset, i.e., \( p(y_t|x) - \frac{1}{K} \sum_{i=1}^K p(y_{t_i}|x) \), where \( K \) is the number of sampling rejected tokens. The discrimination modeled by our TKPO increases during training, indicating the validity of token-level preference comparisons.
H Dataset Example

H.1 Level-of-Detail Dataset Example

<table>
  <tr>
    <th>Article</th>
    <th>Concise Outline</th>
    <th>Moderate Outline</th>
    <th>Comprehensive Outline</th>
  </tr>
  <tr>
    <td>纽约中央公园，藏着美国城市规划的惊世秘密<br>大概只有去过纽约的人，才知道美国城市规划的秘密所在，而这一切都要从纽约的中央公园说起。<br>一边是高耸入云的建筑群，一边是秋意渐浓的中央公园；一半自然，一半尘世；一半风景，一半繁华。纽约中央公园的存在本身被很多人看成是一个奇迹，在这样一个寸土寸金的小岛上，竟然能够有一个这么大的巨型公园。<br>但是在纽约人看来，这其实一点都不矛盾。一百多年前中央公园所在的这块土地还只是一片荒地和几个贫困的小村子，正是因为这么一大块绿地的出现，才拉动了这片土地的价值。如今中央公园周边密布着动辄上亿美元的豪宅，吸引着来自全世界的顶级富豪。不会有哪个官员鬼迷心窍想要把公园的土地用来做商业开发，这样的想法只要一出口，估计就会被几百万人民骂的狗血淋头。<br>纽约中央公园的一个特点是大（占地843英亩，约341公顷），这个大不仅仅只是绝对面积，更在于和整个城市的比例。<br>在一百多年的历史里，纽约中央公园深刻地影响了这个城市里人与人的关系、阶层与阶层的关系，以及人与城市的关系，乃至影响着纽约人的品性，帮助纽约成长为世界最顶级的都市。<br>由 Frederick Law Olmsted 和 Calbert Vaux 两位风景园林设计师设计，于1873年全部建成，历时15年。<br>1858年，中央公园设计竞赛公开举行，奥姆斯特德及沃克斯合作的方案在35个应征方案中脱颖而出，成为中央公园的实施方案。<br>当时的中央公园用地及其周围地区远在纽约市的郊外尚未开发，当时设计师即预料到，将来有一天公园的四周发展起来，这里将是居民唯一可以见到自然风光的地方。<br>中央公园的设计师、景观设计师弗雷德里克·奥姆斯特德(Frederick Olmsted)在陈述他的设计理念时说：“一个城市要想在世界都市里占有一席之地，就必须更加注重人类劳动的更高成果，而不是仅仅注重那些赚钱的行业。他说，“城市里应该有大量的图书馆，教堂，俱乐部和酒店”，不能只为一般的商业服务，也要为“人文，宗教，艺术和学术”服务。”<br>他还说，“公园四周的大楼即使高得比中国的长城高两倍，他的设计也可以保证在园里看不到这些大楼。”他的设计中有山有水，拟营造出一派乡村风光。<br>1903年奥姆斯特德去世之时，纽约人口已达到四百万，而摩天大楼已经林立，早已超过了长城。<br>纽约中央公园设计的三板斧：1. 丰富的功能分区；2. 先进的道路系统；3. 自然的山水布局。<br>作为超级大公园，为了满足各类人群的需要，自然要容纳种种需求。而这就形成了之前从未有过的以人的活动为需求的功能分区。(虽然现在的你也许觉得这是设计的必要性，但这在当时的时代来说，这已经是高瞻远瞩了)<br>纽约中央公园的城市道路系统和景观道路系统也值得一提，由于公园体量的巨大，所以公园在东西向贯穿了4条交通线，和公园形成彼此独立的交通体系。<br>这4条公路无缝衔接了城市交通系统，这已经不单单是景观设计了，这是和城市规划结合的优秀案例。<br>公园内部的景观道路系统也同样独具匠心。园内规划有一条约9.6km长的环形车道，以及比较密集的二级和三级路网。系统组织考虑到能均匀地疏散游人，使游人一进园就能沿着各种道路很快达到自己理想的场所。<br>奥姆斯特德钟爱英国的“田园式”景观，而中央公园与古典园林有个明显的不同，就是最大限度的弱化轴线，基本保持了原有的地貌。这种虽由人作，宛自天开的“无意识”景观非常尊重原始场地。<br>公园大致分为三部分：南部，中心水库，北部。<br>纽约中央公园，名副其实的城市绿肺。<br>这个巨大的绿色空间就像是沙漠里的一片绿洲横躺在曼哈顿中心，即便是在卫星上也能清楚的看到，总面积340公顷，占地150个街区。<br>160年来，中央公园受到严格的法律保护，基本格局未改，变化最大的，一是树木长成了参天大树，二是来往的人群演化了七八个时代，风貌迥异。<br>多年以后 LA + ICONOCLAST 激励来自30个国家的382名设计师重新构思纽约的中央公园。共有193份提交作品参与，竞选奖金$20,000和洛杉矶+的特刊。</td>
    <td>目录<br>1. 纽约中央公园的历史与背景<br>2. 设计与规划理念<br>3. 功能分区与道路系统<br>4. 自然景观与布局<br>5. 中央公园的设计影响与保护<br>6. 未来的重新构想</td>
    <td>目录<br>纽约中央公园的历史与背景<br>- 纽约中央公园的诞生<br>- 设计师与设计理念<br>- 公园的建设与发展<br>- 纽约中央公园的设计特点<br>- 丰富的功能分区<br>- 先进的道路系统<br>- 自然的山水布局<br>- 纽约中央公园的影响<br>- 对城市规划的影响<br>- 对纽约市民生活的影响<br>- 纽约中央公园的保护与未来<br>- 法律保护与基本格局<br>- 未来的重新构想与设计竞赛</td>
    <td>纽约中央公园，藏着美国城市规划的惊世秘密<br>一、中央公园的奇迹<br>1.1 高楼与公园的对比<br>1.2 公园的历史背景<br>1.3 公园对土地价值的影响<br>二、中央公园的设计与理念<br>2.1 设计师与设计过程<br>2.1.1 Frederick Law Olmsted 和 Calbert Vaux<br>2.1.2 设计竞赛与方案选择<br>2.2 设计理念<br>2.2.1 人文、宗教、艺术和科学的服务<br>2.2.2 自然风光的营造<br>三、中央公园的功能与布局<br>3.1 功能分区<br>3.2 道路系统<br>3.2.1 城市道路系统<br>3.2.2 景观道路系统<br>3.3 自然山水布局<br>3.3.1 英国“田园式”景观<br>3.3.2 原始地貌的保留<br>四、中央公园的现状与未来<br>4.1 公园的基本格局与法律保护<br>4.2 公园的演变与人群变化<br>4.3 未来的重新构想<br>4.3.1 LA + ICONOCLAST 的激励<br>4.3.2 设计竞赛与提交作品</td>
  </tr>
</table>

Table 9: Outline level-of-detail controllable generation, Example A.
Central Park in New York: The Astonishing Secret of American Urban Planning
Only those who have been to New York might know the secret of American urban planning, and it all starts with Central Park in New York.
On one side, there are towering skyscrapers; on the other, the autumnal Central Park. Half nature, half urban half scenery, half building site. The very existence of Central Park in New York is seen by many as a miracle. On such a small, valuable island, there is such a large park.
However, for New Yorkers, this is not contradictory at all. Over a hundred years ago, the land where Central Park now stands was just a wasteland and a few impoverished villages. It was the emergence of this vast green space that increased the value of the land. Today, the area around Central Park is filled with luxury residences worth billions of dollars, attracting top real estate, individuals from all around the world. No official would be foolish enough to propose using the park’s land for commercial development; such an idea would likely be met with outrage from millions of people.
One notable feature of Central Park is its size (843 acres, approximately 341 hectares). This size is significant not just in absolute terms but also in relation to the entire city. Over its more than 100-year history, Central Park has profoundly influenced the relationships between people, social classes, and the city itself, even shaping the character of New Yorkers and helping New York grow into one of the world’s top cities.
Designed by landscape architect Frederick Law Olmsted and Calvert Vaux, the park was completed in 1873 after 15 years of construction. In 1858, a design competition for Central Park was held, and the proposal by Olmsted and Vaux was selected from 35 submissions as the implementation plan for the park.
At that time, the land for Central Park and its surrounding areas were undeveloped outskirts of New York City. The designers anticipated that one day the area around the park would develop, making it the only place where residents could see natural scenery.
Frederick Olmsted, the most famous designer of Central Park, stated his design philosophy: “A city is not a place among the other cities; it must focus on the higher achievements of human labor, not just on profit-making industries.” He said, “A city should have numerous libraries, churches, clubs, and hotels,” serving not only commercial purposes but also “humanities, religion, art, and academia.”
He also said, “Even if the buildings around the park were twice as tall as the Great Wall of China, his design would ensure that these buildings would not be visible from within the park.” His design included mountains and water, creating a rural landscape.
By 1920, the population of New York City, New York’s population had reached four million, and skyscrapers were already towering, far surpassing the height of the Great Wall.
The three key elements of Central Park’s design are: 1. Rich functional zoning; 2. Advanced road system; 3. Natural landscape layout.
As a super-large park, it naturally had to accommodate various needs to satisfy different groups of people, forming functional zones based on human activities, which was unprecedented at the time.
The urban road system and landscape road system of Central Park are also noteworthy. Due to the park’s vast size, it is traversed by four east-west traffic lines, forming an independent traffic system from the park.
These four roads seamlessly connect with the city’s traffic system, making it an excellent example of integrating landscape design with urban planning.
The internal landscape road system of the park is also ingeniously designed. The park features a 9.6 km long circular drive and a dense network of secondary and tertiary roads, designed to evenly disperse visitors, allowing them to quickly reach their desired locations upon entering the park.
Olmsted favored the “pastoral” landscape style of England, and Central Park differs significantly from classical gardens by minimizing axial lines and largely preserving the original topography. This “unconscious” landscape, though man-made, appears natural and respects the original site.
The park is roughly divided into three parts: the southern section, the central reservoir, and the northern section.
Central Park in New York truly lives up to its name as the city’s green lung. This vast green space lies like an oasis in the heart of Manhattan, clearly visible even from satellites above. It covers a total area of 340 hectares, spanning 150 city blocks.
For 160 years, Central Park has been strictly protected by law, with its basic structure unchanged. The most significant changes are the trees growing into towering giants and the evolving groups of visitors over seven or eight generations, each with distinct characteristics.
Years later, LA + ICONOCLAST inspired 382 designers from 30 countries to reimagine New York’s Central Park. A total of 193 submissions were received, competing for a $20,000 prize and a special issue of LA+.

<table>
  <tr>
    <th>Article</th>
    <th>Concise Outline</th>
    <th>Moderate Outline</th>
    <th>Comprehensive Outline</th>
  </tr>
  <tr>
    <td>Central Park in New York: The Astonishing Secret of American Urban Planning</td>
    <td>Outlines<br>1. History and Background of Central Park in New York<br>2. Design and Planning Concepts<br>3. Functional Zoning and Road System<br>4. Natural Landscape and Layout<br>5. Impact and Preservation of Central Park<br>6. Future Reimagining</td>
    <td>Outlines<br>History and Background of Central Park in New York<br>- The Birth of Central Park in New York<br>- Designers and Design Concepts<br>- Construction and Development of the Park<br>Design Features of Central Park in New York<br>- Rich Functional Zoning<br>- Advanced Road System<br>- Natural Landscape Layout<br>Impact of Central Park in New York<br>- Impact on Urban Planning<br>- Impact on the Lives of New York Citizens<br>Preservation and Future of Central Park in New York<br>- Legal Protection and Basic Structure<br>- Future Reimagining and Design Competitions</td>
    <td>Central Park in New York: The Astonishing Secret of American Urban Planning<br>I. The Miracle of Central Park<br>1.1 Contrast Between Skyscrapers and the Park<br>1.2 Historical Background of the Park<br>1.3 Impact of the Park on Land Values<br>II. Design and Concepts of Central Park<br>2.1 Designers and Design Process<br>2.1.1 Frederick Law Olmsted and Calvert Vaux<br>2.1.2 Design Competition and Selection of Proposals<br>2.2 Design Concepts<br>2.2.1 Service to Humanities, Religion, Art, and Academia<br>2.2.2 Creation of Natural Sites<br>III. Functions and Layout of Central Park<br>3.1 Functional Zoning<br>3.2 Road System<br>3.2.1 Urban Road System<br>3.2.2 Landscape Road System<br>3.3 Natural Landscape Layout<br>3.3.1 English “Pastoral” Landscape<br>3.3.2 Preservation of Original Topography<br>IV. Current Status and Future of Central Park<br>4.1 Basic Structure and Legal Protection of the Park<br>4.2 Evolution of the Park and Changes in Visitors<br>4.3 Future Reimagining<br>4.3.1 Inspiration from LA + ICONOCLAST<br>4.3.2 Design Competition and Submissions</td>
  </tr>
</table>

Table 10: Outline level-of-detail controllable generation, Example A. (Translation)
<table>
  <tr>
    <th>Article</th>
    <th>Concise Outline</th>
    <th>Moderate Outline</th>
    <th>Comprehensive Outline</th>
  </tr>
  <tr>
    <td>鸟山明去世，大概是是我们第一次经历陪着我们所有人成长的漫画家离去。</td>
    <td>目录<br>1. 鸟山明的离世<br>2. 日本漫画的源流<br>3. 漫画创作的瓶颈<br>4. 鸟山明的突破<br>5. 《龙珠》的成功<br>6. 鸟山明的遗产<br>7. 结语</td>
    <td>目录<br>鸟山明的影响与地位<br>日本漫画的源流<br>漫画创作的瓶颈<br>鸟山明的突破<br>《龙珠》的成功<br>鸟山明的遗产<br>结语</td>
    <td>鸟山明去世，陪伴我们成长的漫画大师<br>一、引言<br>1. 鸟山明的影响力<br>2. 漫画大师的离去<br>二、漫画大师的时代背景<br>1. 手冢治虫的影响<br>1.1 手冢治虫的去世<br>1.2 手冢治虫与迪士尼的关系<br>2. 藤子F不二雄的去世<br>3. 互联网前后的漫画家<br>3.1 三浦健太郎的病逝<br>3.2 原产名子的自杀<br>三、鸟山明的创作历程<br>1.《阿拉蕾》的成功<br>1.1《阿拉蕾》的影响力<br>1.2 鸟山明的生活方式<br>2.《龙珠》的诞生<br>2.1《龙珠》连载初期的低迷<br>2.2 鸟山明与编辑鸟岛和彦的合作<br>2.3 成龙电影的启发<br>四、《龙珠》的成功与影响<br>1.《龙珠》的画面创新<br>1.1 动感画面的创作<br>1.2 成龙功夫的应用<br>2.《龙珠》的成功<br>2.1 重返《少年Jump》榜首<br>2.2 连载的结束与鸟山明的退隐<br>3.《龙珠》的艺术成就<br>3.1 流畅的故事<br>3.2 细致的机械与动物绘画<br>3.3 天真单纯的角色<br>五、鸟山明的遗产与影响<br>1.动感画面的传承与失传<br>2.鸟山明对后辈漫画家的期许<br>2.1 2019年《少年Jump》创刊50周年对谈<br>2.2 鸟山明对漫画家创作的期望<br>六、结语<br>1.鸟山明的天才与傻瓜精神<br>2.鸟山明的永恒影响</td>
  </tr>
  <tr>
    <td>手冢治虫是1989年去世的，藤子F不二雄是1996年去世的，都是前互联网时期。在这之后，花开花落，花落花开，漫画家们生生不息，偶尔有三浦健太郎病逝或声原妃名子自杀的消息，但总体来说和大众无关——但鸟山明不一样，他的《阿拉蕾》和《龙珠》是全世界最具影响力的漫画，他是手冢之后对漫画这种内容形态进行升级的最重要的创作者，绝对的一代宗师。</td>
    <td>日本漫画的创作源流，是手冢治虫。手冢学习的则是迪士尼，他去电影院连看了120遍《小鹿斑比》，把每个分镜印在脑子里。从此之后，日本漫画有了自己的镜头语言。</td>
    <td>这之后约三十年，漫画家们涉猎题材越来越广，画风越来越多样化，镜头语言的运用越来越大胆娴熟。但始终有一个瓶颈：画面是静态的，缺乏动感。以鸟山明同期的最著名的漫画家车田正美、原哲夫这两位为例：战斗时，角色几乎都是静态摆一个帅气的姿势，然后对手在下一格分镜里做出受伤的姿势。</td>
    <td>鸟山明无意间改变这一切的。</td>
  </tr>
  <tr>
    <td>1984年，他画的《阿拉蕾》已经红遍日本。住在爱知乡下，平时爱养猫养狗拼装玩具的鸟山明并不是很珍惜这成功——1983年他的个人所得税是9698万日元（别信网上说6亿多日元那个版本），相当于当时的80万人民币，在日本人里排名111名，绝对挣够了——他只想停掉这个日进斗金的连载。</td>
    <td>虽然性格谦逊，但他终归是昭和一代人，吃苦耐劳、服从要求的底色是在的。在编辑的要求下，他还是启动了自己的新连载《龙珠》。</td>
    <td>今天几乎所有公认《龙珠》是日本漫画史上最成功、最赚钱的作品，但连载之初，它的人气有点低迷，掉到了它所连载的《少年Jump》杂志排名的十名开外。《少年Jump》是日本最成功也最卷的杂志，每期做读者问卷排行榜，排名靠后的作品，无论作者多大牌都会被腰斩。</td>
    <td>鸟山明的编辑鸟岛和彦说，那段时间他每天都跟鸟山明通电话，讨论该怎么办。电话里聊到一个琐事：鸟山明画画时，身后总是开着电视，放录像带，听到自己有兴趣的段落，他就回身去看两眼。鸟岛问：你最近都看什么啊？鸟山明说：成龙，看了几十遍。</td>
  </tr>
  <tr>
    <td>从这个对话开始，鸟山明和鸟岛商量出来一个方案：不像传统一样凹造型，嘴里大喊一些很牛的必杀技名字了，改成龙式的功夫，让画面动起来。</td>
    <td>这话听起来容易，也真得亏鸟山明是个画画天才，他真的画出了这种行云流水、拳拳到肉、亦庄亦谐的感觉。</td>
    <td>之后就是历史了。《龙珠》很快重返Jump排名榜首，直到鸟山明又用很多年费了很大力气终于成功游说编辑部停掉连载。他从此功成身退，不再做长篇连载，继续住在乡下，据说养了许多猫狗和乌龟。</td>
    <td>当然，一部集大成漫画的成功，不会只因为画面创新。《龙珠》是那种，时隔30年看，依然堪称完美的作品。有太多东西可以夸，比如鸟山明的故事极其流畅，这种品质在商业作品连载中其实很少见；比如鸟山明极擅长画机械和动物，他画中的很多细节让人赞叹有趣且神乎其技；比如鸟山明笔下的角色都很天真单纯，一个人看的作品越多，在社会上摸爬滚打越多，越知道这些有赤子之心的角色何其珍贵。甚至，鸟山明所开创的极富动感的画面，虽然广受模仿，但没有任何作品敢说自己达到了媲美《龙珠》的程度，他的技艺说是失传了也不算夸张。</td>
  </tr>
  <tr>
    <td>2019年《少年Jump》创刊50周年之际，邀请鸟山明和井上雄彦做了一次罕见的对谈，提问者问鸟山明对今天的漫画家有什么期许，鸟山明的回答大概是：现在的漫画家画的都太好了，我更想看到那种，让人看的时候一边大笑一边觉得这个人是傻瓜的作品。</td>
    <td>熟悉鸟山明的读者都知道，他自己就是这样一个傻瓜。但只有这样的傻瓜，才能成为不世出天才。</td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 11: Outline level-of-detail controllable generation, Example B.
The passing of Akira Toriyama marks perhaps the first time we’ve experienced the loss of a manga artist who grew alongside all of us. Osamu Tezuka passed away in 1989, and Fujiko F. Fujio died in 1996, both before the Internet era. After that, seasons change, one flower falls and another blooms and fades, but the passing of Toriyama was a way of life for many of us. The untimely death or Hiromu Arakawa’s tragic passing, but generally, these were distant events to the public. But Toriyama is different. His Dr. Slump and Dragon Ball series have influenced generations of artists and fans, and he is the creator in the evolution of manga as a medium after Tezuka, a true master of his generation.

The creation lineage of Japanese manga originates with Osamu Tezuka. Tezuka was inspired by Disney, having watched Bambi 120 times in theaters, committing every storyboard to memory. From that point on, Japanese manga developed its own cinematic language.

Over the decades and decades, manga artists explored a wider range of themes, developed diverse artistic styles, and became increasingly bold and skilled with their use of cinematic techniques. But there was always a limitation: the images were still drawn in panels, and the story was fixed. In the next panel, a combat between Masami Kurumada and Tetsuo Hara – during battle scenes, characters were often frozen in dramatic poses, while in the next panel, the opponent would show signs of injury.

Toriyama unintentionally changed all of this.

In 1984, his Dr. Slump became a nationwide hit in Japan. Living in rural Aichi Prefecture, Toriyama, who enjoyed keeping pets and dismantling model kits, didn’t seem like the kind of person who would become a billionaire. With a small salary (not the exaggerated ¥600 million version often found online), equivalent to about ¥800,000 at the time, placing him 11th among Japanese celebrities—he had certainly earned enough! Still, he just wanted to stop the repetitive serialization of Dragon Ball Z, the product of the Shonen Jump, where hard work and obedience to requests were ingrained. At the request of his editor, he began his new serialization Dragon Ball.

Toriyama was widely recognized as the most successful and profitable manga in Japanese history. However, when it first began serialization, its popularity was initially sluggish, ranking outside the top ten in the Shonen Jump magazine, where it was published. Shonen Jump is the most successful and competitive manga magazine in Japan. If a new manga doesn’t sell well, it will be canceled. If it falls behind, no matter how famous the author, risks being canceled.

Toriyama’s editor, Yoshihiro Torishima, recalled that during this time, he spoke with Toriyama once a week, and they would discuss the series. One day, they had a casual conversation: Toriyama mentioned that when drawing, he always had the TV on, watching videos, and when he heard something interesting, he would say, “I’ll draw that!” Torishima asked, “Have you been watching Jackie Chan recently?” Toriyama replied, “Jackie Chan. I’ve watched it dozens of times.” From this conversation, Toriyama and Torishima came up with a new approach: instead of sticking to the traditional static poses and shouting overly dramatic attack names, they decided to incorporate Jackie Chan-style kung fu, making the action more fluid.

It sounds simple, but Toriyama’s genius is responsible for his ability to depict this smooth, punchy, and fluid action with both his art and his humor.

What followed was history. Dragon Ball quickly regained its top position in the Shonen Jump rankings, and Toriyama continued working on the series until, after many years of effort, he successfully persuaded the editorial team to end the serialization. After the success in Dragon Ball, he realized that formalization and returned to his rural life, reportedly taking care of many cats, dogs, birds, and turtles.

Of course, the success of such a landmark manga wasn’t solely due to the innovation in its visuals. Dragon Ball remains a masterpiece that, even 30 years later, is still considered nearly perfect. There is so much to praise, such as Toriyama’s seamless storytelling, his ability to create an uncanny similarity in completely unrelated worlds, his exceptional skill in drawing machinery and animals, with many details that are both impressive and ingenious; and the pure, innocent characters he created. The more a person reads, the more they understand just how rare these characters with a childlike innocence. Even Toriyama’s dynamic and fluid art, which is unmatched has yet to be surpassed by any other work. To say his skills have been lost would not be an exaggeration.

In honor of the 50th anniversary of Shonen Jump, Toriyama participated in a rare conversation with Takehiko Inoue. When asked what expectations he had for today’s manga artists, Toriyama replied, “The manga artists today draw so well. What I want to see is something that makes people laugh and think, ‘This person is a genius.’” Readers familiar with Toriyama know that he is indeed that kind of fool. But only such a fool can become a once-in-a-lifetime genius.

<table>
  <tr>
    <th>Article</th>
    <th>Concise Outline</th>
    <th>Moderate Outline</th>
    <th>Comprehensive Outline</th>
  </tr>
  <tr>
    <td>The passing of Akira Toriyama marks perhaps the first time we’ve experienced the loss of a manga artist who grew alongside all of us. Osamu Tezuka passed away in 1989, and Fujiko F. Fujio died in 1996, both before the Internet era. After that, seasons change, one flower falls and another blooms and fades, but the passing of Toriyama was a way of life for many of us. The untimely death or Hiromu Arakawa’s tragic passing, but generally, these were distant events to the public. But Toriyama is different. His Dr. Slump and Dragon Ball series have influenced generations of artists and fans, and he is the creator in the evolution of manga as a medium after Tezuka, a true master of his generation.

The creation lineage of Japanese manga originates with Osamu Tezuka. Tezuka was inspired by Disney, having watched Bambi 120 times in theaters, committing every storyboard to memory. From that point on, Japanese manga developed its own cinematic language.

Over the decades and decades, manga artists explored a wider range of themes, developed diverse artistic styles, and became increasingly bold and skilled with their use of cinematic techniques. But there was always a limitation: the images were still drawn in panels, and the story was fixed. In the next panel, a combat between Masami Kurumada and Tetsuo Hara – during battle scenes, characters were often frozen in dramatic poses, while in the next panel, the opponent would show signs of injury.

Toriyama unintentionally changed all of this.

In 1984, his Dr. Slump became a nationwide hit in Japan. Living in rural Aichi Prefecture, Toriyama, who enjoyed keeping pets and dismantling model kits, didn’t seem like the kind of person who would become a billionaire. With a small salary (not the exaggerated ¥600 million version often found online), equivalent to about ¥800,000 at the time, placing him 11th among Japanese celebrities—he had certainly earned enough! Still, he just wanted to stop the repetitive serialization of Dragon Ball Z, the product of the Shonen Jump, where hard work and obedience to requests were ingrained. At the request of his editor, he began his new serialization Dragon Ball.

Toriyama was widely recognized as the most successful and profitable manga in Japanese history. However, when it first began serialization, its popularity was initially sluggish, ranking outside the top ten in the Shonen Jump magazine, where it was published. Shonen Jump is the most successful and competitive manga magazine in Japan. If a new manga doesn’t sell well, it will be canceled. If it falls behind, no matter how famous the author, risks being canceled.

Toriyama’s editor, Yoshihiro Torishima, recalled that during this time, he spoke with Toriyama once a week, and they would discuss the series. One day, they had a casual conversation: Toriyama mentioned that when drawing, he always had the TV on, watching videos, and when he heard something interesting, he would say, “I’ll draw that!” Torishima asked, “Have you been watching Jackie Chan recently?” Toriyama replied, “Jackie Chan. I’ve watched it dozens of times.” From this conversation, Toriyama and Torishima came up with a new approach: instead of sticking to the traditional static poses and shouting overly dramatic attack names, they decided to incorporate Jackie Chan-style kung fu, making the action more fluid.

It sounds simple, but Toriyama’s genius is responsible for his ability to depict this smooth, punchy, and fluid action with both his art and his humor.

What followed was history. Dragon Ball quickly regained its top position in the Shonen Jump rankings, and Toriyama continued working on the series until, after many years of effort, he successfully persuaded the editorial team to end the serialization. After the success in Dragon Ball, he realized that formalization and returned to his rural life, reportedly taking care of many cats, dogs, birds, and turtles.

Of course, the success of such a landmark manga wasn’t solely due to the innovation in its visuals. Dragon Ball remains a masterpiece that, even 30 years later, is still considered nearly perfect. There is so much to praise, such as Toriyama’s seamless storytelling, his ability to create an uncanny similarity in completely unrelated worlds, his exceptional skill in drawing machinery and animals, with many details that are both impressive and ingenious; and the pure, innocent characters he created. The more a person reads, the more they understand just how rare these characters with a childlike innocence. Even Toriyama’s dynamic and fluid art, which is unmatched has yet to be surpassed by any other work. To say his skills have been lost would not be an exaggeration.

In honor of the 50th anniversary of Shonen Jump, Toriyama participated in a rare conversation with Takehiko Inoue. When asked what expectations he had for today’s manga artists, Toriyama replied, “The manga artists today draw so well. What I want to see is something that makes people laugh and think, ‘This person is a genius.’” Readers familiar with Toriyama know that he is indeed that kind of fool. But only such a fool can become a once-in-a-lifetime genius.</td>
    <td>
      <ol>
        <li>The Passing of Akira Toriyama</li>
        <li>The Origins of Japanese Manga</li>
        <li>The Bottleneck in Manga Creation</li>
        <li>Akira Toriyama’s Breakthrough</li>
        <li>The Evolution of Japanese Manga</li>
        <li>Osamu Tezuka’s Cinematic Techniques</li>
        <li>The Diversification of Manga Themes and Art Styles</li>
        <li>The Bottleneck of Dynamic Imagery</li>
        <li>Akira Toriyama’s Innovation and Success</li>
        <li>The Success of Dr. Slump</li>
        <li>The Beginnings and Challenges of Dragon Ball</li>
        <li>The Influence of Jackie Chan Kung Fu on Dragon Ball</li>
        <li>The Success and Impact of Dragon Ball</li>
        <li>Akira Toriyama’s Personal Life and Creative Philosophy</li>
        <li>Akira Toriyama’s Lifestyle</li>
        <li>The Unique Characters of Dragon Ball</li>
        <li>Akira Toriyama’s Expectations for Manga Artists</li>
        <li>Akira Toriyama’s Genius and Foolish Spirit</li>
      </ol>
    </td>
    <td>
      <ol>
        <li>The Influence and Status of Akira Toriyama</li>
        <li>The Passing and Impact of Akira Toriyama</li>
        <li>The Era of Osamu Tezuka and Fujiko F. Fujio</li>
        <li>The Influence of Osamu Tezuka</li>
        <li>The Passing of Osamu Tezuka</li>
        <li>The Relationship Between Osamu Tezuka and Disney</li>
        <li>The Passing of Fujiko F. Fujio</li>
        <li>Manga Artists Before and After the Internet</li>
        <li>The Passing of Kentaro Miura</li>
        <li>The Suicide of Hinako Ashihara</li>
        <li>Akira Toriyama’s Creative Journey</li>
        <li>The Success of Dr. Slump</li>
        <li>Akira Toriyama’s Lifestyle</li>
        <li>The Birth of Dragon Ball</li>
        <li>The Initial Struggles of Dragon Ball</li>
        <li>The Collaboration Between Akira Toriyama and Kazuhiko Torishima</li>
        <li>Introduction from Jackie Chan Movies</li>
        <li>The Success and Impact of Dragon Ball</li>
        <li>The Visual Innovations of Dragon Ball</li>
        <li>The Creation of Dynamic Imagery</li>
        <li>The Application of Jackie Chan-style Kung Fu</li>
        <li>The Commercial Success of Dragon Ball</li>
        <li>Returning to the Top of Shonen Jump</li>
        <li>The End of Serialization and Akira Toriyama’s Retirement</li>
        <li>The Artistic Achievements of Dragon Ball</li>
        <li>Smooth Storytelling</li>
        <li>Technical and Animal Drawings</li>
        <li>Innocent and Simple Characters</li>
        <li>The Legacy and Influence of Akira Toriyama</li>
        <li>The Influence and Loss of Dynamic Imagery</li>
        <li>Akira Toriyama’s Expectations for Future Manga Artists</li>
        <li>Interviews and Interviews</li>
        <li>Akira Toriyama’s Expectations for Manga Creation</li>
        <li>Conclusion</li>
        <li>Akira Toriyama’s Genius and Foolish Spirit</li>
        <li>The Eternal Influence of Akira Toriyama</li>
      </ol>
    </td>
    <td>
      <ol>
        <li>Akira Toriyama’s Passing: The Manga Master Who Accompanied Our Growth</li>
        <li>Introduction</li>
        <li>The Influence of Akira Toriyama</li>
        <li>The Passing of a Manga Master</li>
        <li>The Era of the Manga Master</li>
        <li>The Influence of Osamu Tezuka</li>
        <li>The Passing of Osamu Tezuka</li>
        <li>The Relationship Between Osamu Tezuka and Disney</li>
        <li>The Passing of Fujiko F. Fujio</li>
        <li>Manga Artists Before and After the Internet</li>
        <li>The Passing of Kentaro Miura</li>
        <li>The Suicide of Hinako Ashihara</li>
        <li>Akira Toriyama’s Creative Journey</li>
        <li>The Success of Dr. Slump</li>
        <li>Akira Toriyama’s Lifestyle</li>
        <li>The Birth of Dragon Ball</li>
        <li>The Initial Struggles of Dragon Ball</li>
        <li>The Collaboration Between Akira Toriyama and Kazuhiko Torishima</li>
        <li>Introduction from Jackie Chan Movies</li>
        <li>The Success and Impact of Dragon Ball</li>
        <li>The Visual Innovations of Dragon Ball</li>
        <li>The Creation of Dynamic Imagery</li>
        <li>The Application of Jackie Chan-style Kung Fu</li>
        <li>The Commercial Success of Dragon Ball</li>
        <li>Returning to the Top of Shonen Jump</li>
        <li>The End of Serialization and Akira Toriyama’s Retirement</li>
        <li>The Artistic Achievements of Dragon Ball</li>
        <li>Smooth Storytelling</li>
        <li>Technical and Animal Drawings</li>
        <li>Innocent and Simple Characters</li>
        <li>The Legacy and Influence of Akira Toriyama</li>
        <li>The Influence and Loss of Dynamic Imagery</li>
        <li>Akira Toriyama’s Expectations for Future Manga Artists</li>
        <li>Interviews and Interviews</li>
        <li>Akira Toriyama’s Expectations for Manga Creation</li>
        <li>Conclusion</li>
        <li>Akira Toriyama’s Genius and Foolish Spirit</li>
        <li>The Eternal Influence of Akira Toriyama</li>
      </ol>
    </td>
  </tr>
</table>

Table 12: Outline level-of-detail controllable generation, Example B. (Translation)
H.2 Language Style Dataset Example

<table>
  <tr>
    <th>Article</th>
    <th>Literary Outline</th>
    <th>Objective Outline</th>
  </tr>
  <tr>
    <td>《景观社会》“影像化”的社会景观<br>(一) 从商品批判到景观批判<br>德波在《景观社会》中从不同角度对景观概念做了论说。景观，原意为一种被展现出来的可视觉的客体或景象，也指一种主体性的、有意识的表演和做作。德波借其概括自己看到的当代资本主义社会新特质，即当代社会存在的主导本质体现为一种被展现的图景性。人们因为对景观的迷恋而丧失对真实生活的渴望和要求，而资本家则依靠控制景观的生成和变换来操纵社会生活。<br>德波沿着马克思的理论思路确定了自己的理论前提：当代发达工业社会已经是个景观堆聚的社会。德波对马克思的商品现象学作了改写，把商品异化改写为景观异化。其把到目前为止的资本主义社会分为商品社会和景观社会两个阶段。商品社会以占有为特征，景观社会以展示为特征。它们的不同在于资本增值的中介：以前，资本通过操作具体的物品来控制社会；而当代，资本通过操作象征生活。<br>在德波这里，景观是一种由感性的可观看性建构起来的幻象，它的存在由表象所支撑，以各种不同的影像为其外部显现形式。尤为重要的是，景观的场是在社会本真存在的遮蔽。后来，波德里亚又在此基础上发展了类象一元，后者所谓的类象的定位是存在论意义上的根本差异点，因为它比原本存在更加真实。<br>在德波眼中，景观的出现并不就意味着世界已被虚化为一幅影像图景。“景观不能被理解为一种由大众传播技术制造的视觉欺骗，必须充分理解‘景观不是影像的聚集，而是以影像为中介的人们之间的社会关系’。在今天的资本主义社会里，物质生产方式的决定性结构已经转向以影像方式为主导的景观生产方式。”<br>(二) 视觉传播化的现代社会统治<br>德波认为，景观的本质是拒斥对话。景观是一种更深层的无形控制，它消解了主体的反抗和批判的可能性。在景观的迷人之中，人只能单向度地默认。如是，方为意识形态的本质。首先，景观已经成为当前资本主义生产方式的目标，或者叫“现实社会非现实的核心”，更通俗地说，景观已然成为现今人们“主导性的生活模式”。相比于过去人们对吃穿住行等物性目标的追求而言，今天的人们在生活目标和生活模式上已经发生了翻天覆地的变化，如今我们所追求的，是一种让人目眩的景观秀。这一点在现代人对新闻、广告和娱乐等的大量非本真的需要中得到了突出的体现。<br>人之存在不再由自己的真实的需要构成，而是由景观所指向的展示性目标和异化性的需要堆积而成。现代工业社会的基础已经不再是传统社会中物质生产物品与消费的真实关系了，而是景观，是由视觉映像来统领经济的秩序。所以，真实的目标（这包括社会历史的前进目标和人的需要）早已烟消云散，景观就是一切，景观就是目标。<br>其次，是景观的意识形态功能。德波认为，景观的存在和统治性的布展恰恰证明了今日资本主义体制的合法性。人们在对景观的顺从中无意识地肯定着现实的统治。所以，景观也是当代资本主义合法性的“永久在场”。这话指出了景观的意识形态功能，具体包含三个方面。<br>一是它通过肯定性的表象，将人们锚定于资本家在生产和消费中“已做出的选择”。换句话说，如今，我们在生活的每个细节情境中，都不得不在广告炫目的情景牵引下，不自觉地面对一个已经被装饰过的欲望对象世界。在广告的统治下，我们无能为力，更无处可逃。在购买景观和对景观生活方式的无意识顺从中，我们直接肯定着现存体制。<br>第二，通过审查而展现出来的景观，也必然是现体制合法性的同谋。景观，当然是一种隐性的意识形态。换句话说，无论是通过广告，还是通过其他影像呈现在我们面前的各种景观，其本质都是在认同性的、或者是无意识地支配着人们的欲望结构。我们以对商品疯狂的追逐来肯定资本主义的市场体制，或者在影像文化的引诱下，将现存的资产阶级生活方式误认为本真的存在方式，自愿成为五体投地的奴隶。<br>其三，景观还通过支配生产之外的大部分时间来达到对现代人的全面控制，这也是德波关于当代资本主义统治新形式的一个新发现，即对于非劳动时间的控制。景观的捕捉对象恰恰是生产之外人的闲暇时间。景观的无意识心理文化控制和对人的虚假消费的制造，都是在生产之外的时间中悄然发生的。由此，资本主义对于人的统治在空间和时间上都大大扩展了。并且，也正是由于景观能在一切闲暇时间中对人发生颠倒性欲望驱动，才使物质生产更加远离人之真实需要，从而更直接服务于资本的剩余价值增值。<br>(三) 景观的统治形式<br>关于景观的统治形式的问题，德波将景观区分为两种主要形式：集中的景观和弥散的景观。“集中的景观从根本上与官僚主义/资本主义相联结。”这是一个理论定位。作为一种技术而言，所谓的集中的景观可能是由欠发达社会在试图增强国家权力时引入的，或者是在发达资本主义国家中特定的危急时刻出现的。从本质上讲，集中的景观就是官僚政治专政的工具。例如，纳粹时期的法西斯国家就是集中景观的典型。所谓弥散的景观，也就是景观的一般形式，被德波指认为当代资本主义的社会控制性形式及其意识形态。<br>1988年，德波在《关于＜景观社会＞的评论》中提出了一种新的景观形式，即综合的景观。作为一种独裁，景观与过去的暴政不同，它常常呈现为某种甜蜜的意识形态控制。经过20多年的发展，景观社会不仅未曾有丝毫的削弱，相反，通过理性地整合前两种形式，景观的“第三种形式”又建立起来，以这一形式的普遍胜利为基础，它越来越展现出自己的强壮和扩散。这就是综合的景观，自此以后它将自己强加于全球。对于当今这一综合，德波强调了其无所不在性。景观，已经“渗透到全部实在”。资本主义的全球化就是景观的全球化。<br>与此同时，德波也进一步指认综合景观的五个主要特征：“不间断的科学技术的更新；国家和经济的一体化；普遍化的秘密；无可置疑的谎言；永恒在场。”这五个特征揭示了综合景观融合了弥散景观中的隐蔽特性和集中景观中的控制力，使其呈现出难以拒绝的影响力，并使它成为“意识形态的顶点”，极力推动景观成为合法化的社会现实。</td>
    <td>目录<br>1. 从商品批判到景观批判<br>2. 视觉传播化的现代社会统治<br>3. 景观的统治形式</td>
    <td>目录<br>一、从商品批判到景观批判<br>1.1 景观的概念与特质<br>1.2 景观社会的形成<br>1.3 景观的幻象与社会关系<br>二、视觉传播化的现代社会统治<br>2.1 景观的本质与拒斥对话<br>2.2 景观作为生活模式<br>2.3 景观的意识形态功能<br>2.3.1 资本家的选择与广告统治<br>2.3.2 审查与合法性<br>2.3.3 对非劳动时间的控制<br>三、景观的统治形式<br>3.1 集中的景观<br>3.2 弥散的景观<br>3.3 综合的景观<br>3.3.1 综合景观的特征<br>3.3.2 综合景观的全球化与影响力</td>
  </tr>
</table>

Table 13: Outline language style controllable generation, Example A.
The Society of the Spectacle | The "Vocalized" Social Landscape

(1) From Commodity Critique to Spectacle Critique
In The Society of the Spectacle, Guy Debord discusses the concept of spectacle from various perspectives. Originally, spectacle referred to a visible, objective scene or landscape, something displayed for viewing, but it also implies a subjective, conscious performance or show. Debord uses this to describe the new features of contemporary capitalist society, arguing that the dominant essence of modern societies is not as a commodity society. People, captured by the "accursed" supply and demand for an authentic life, while capitalists manipulate the entire social life by controlling the production and transformation of spectacles.

Debord follows Marx's theoretical trajectory in defining his own theoretical premises: contemporary advanced industrial society has become a society of accelerated spectacles. Debord rewrote Marx's commodity phenomenology, transforming the alienation of commodities into the alienation of life. People are divided into two groups: those who simply exist and those who are characterized by possession, and the spectacle society, characterized by display. Their difference lies in the mediation of capital accumulation: in the past, capital controlled society by manipulating physical goods, while today, capital controls life through the manipulation of spectacles.

For Debord, the spectacle is an illusion constructed by sensory visibility, supported by representations, and manifested in various images. Contemporary spectators of the spectacle are thus trapped in a world of "phantom realities," a "simulacrum" based on this idea, which, in an ontological sense, represents a fundamental substitution, being more "real" than reality itself.

In Debord's view, the emergence of the spectacle does not imply that the world has been reduced to an image or a visual tableau. "The spectacle should not be understood as a visual deception created by mass media technologies." Debord insists, emphasizing that "the spectacle is not the image of reality; it is reality. It is a reality which is constructed by the media between people and their images." Today's capitalist society, the decisive structure of material production has shifted to a spectacle-driven production system, dominated by visual images.

(2) The Visual Communication of Modern Social Domination
Debord argues that the essence of the spectacle is to reject dialogue. The spectacle represents a deeper, invisible form of control, dissolving the subject's ability to respond and negate. In the fascination with the spectacle, individuals are compelled to accept a one-dimensional view of life. In this sense, the essence of the spectacle is the rejection of dialogue.

First, the spectacle has become the goal of contemporary capitalist production, or what Debord calls "the unreal core of real society." In simpler terms, the spectacle has become the dominant mode of life for today's people. Compared to the past, when people pursued material goods like food, clothing, shelter, and transportation, today's people have undergone a revolutionary shift in their life goals and modes. Now, the goal of life is to be entertained. This is most evident in the modern insatiable appetite for news, advertising, and entertainment, all of which are inherently aesthetic.

Human existence is no longer defined by real needs but is instead shaped by the spectacle's performative goals and alienated desires. The foundation of modern industrial society is no longer the authentic relationship between material production and consumption, but the spectacle's ability to create and maintain the illusion that the real world is the sole objective. Hence, the true nature of both social-historical progress and human needs—have long since disappeared, leaving the spectacle as the only reality, the sole objective.

Second, the ideological function of the spectacle is crucial. Debord argues that the very existence and pervasive arrangement of spectacles confirm the legitimacy of the contemporary capitalist system. People, through their passive acceptance of the spectacle, unwittingly affirm the ideology of the spectacle society. The spectacle is thus a "permanent presence" of capitalism's legitimacy. This assertion addresses the ideological function of the spectacle, which operates in two key ways:

1. The spectacle anchors people to the "choices already made" by capitalists in production and consumption. In other words, today, in every detail of life, people are unconsciously led to face a world of desired objects that have already been adorned by advertising. Under the rule of advertising, we are powerless and have no escape. By purchasing into the spectacle and unconsciously submitting to its lifestyle, we also affirm the ideology of the spectacle.

2. The spectacle, as presented through media scrutiny, is inherently a complicity in the legitimacy of the current system. The spectacle is, of course, a form of covert ideology. Whether through advertising or other visual forms, the spectacle subtly manipulates people's desires. By churning consciousness, we affirm the capitalist market system, or under the allure of image culture, we mistake the bourgeois lifestyle for authentic existence.

3. The spectacle also controls most of the time outside of productive labor, which Debord identifies as a new form of capitalist domination. The spectacle targets leisure time, the time when people are away from work. The unconscious psychological and cultural control exerted by the spectacle and its techniques of false consumption happens subtly during non-working hours. As such, capitalism's domination has extended beyond socially and temporally. And it is precisely because the spectacle can drive inverted desires during all leisure time that material production becomes increasingly disconnected from real human needs, instead directly serving the surplus value of capital.

(3) The Forms of Spectacle Domination
Regarding the forms of spectacle domination, Debord distinguishes between two primary forms: concentrated spectacle and diffuse spectacle. "The concentrated spectacle is a function of limited resources and a highly centralized state power. The diffuse spectacle, in contrast, concentrated spectacle may be introduced by underdeveloped societies attempting to strengthen state power or may emerge in developed capitalist countries during specific crises. Essentially, the concentrated spectacle is a tool of bureaucratic political dictatorship. For example, Nazi fascism is a typical example of concentrated spectacle. The diffuse spectacle, in contrast, represents the general form of the spectacle as it is seen by Debord. The underlying logic is the sociology of contemporary culture."

In 1988, in his book Notes on the Society of the Spectacle, Debord introduces a new form of spectacle, the integrated spectacle. Unlike past tyrannies, the integrated spectacle presents itself as a form of sweet ideological control. Over the course of more than two decades, the spectacle society has not weakened; rather, by rationally integrating the first two forms, a "third form" of the spectacle has emerged. Based on the genuine victory of this form, it increasingly demonstrates its strength and diffusion. Thus, the integrated spectacle, which has replaced the individual and collective forms of the spectacle, is the dominant form of the spectacle. The integrated spectacle is "all reality," and the globalization of capitalism is synonymous with the globalization of the spectacle.

Simultaneously, Debord identifies five key features of the integrated spectacle: "the continuous renewal of science and technology; the integration of the state and economy; the universalization of secrecy; irrefutable lies; and perpetual presence." These five features reveal how the integrated spectacle combines the hidden nature of the diffuse spectacle with the control power of the concentrated spectacle, making it increasingly persuasive and turning it into "the apex of ideology," pushing the spectacle to become the legitimized social reality.
今天，让我们在塞纳河的落日余晖中相见。
巴黎当地时间26日晚7时30分，北京时间27日凌晨1时30分，2024年巴黎奥运会开幕式将在塞纳河上举行。
这将会是夏季奥运会历史上首次在体育场外举行开幕式，呼应了作家海明威笔下的巴黎——“一席流动的盛宴”。与此同时，也是对“奥运更开放”口号的生动践行。
那么，在这场被称为前所未有的奥运会开幕式上，我们都能看到些什么？
逾万名运动员巡游入场
本次巴黎奥运会开幕式，运动员将沿着贯穿巴黎市中心那条流光溢彩的河流与观众相见。沿着塞纳河的巡游路线堪称是一场穿越巴黎历史和建筑的视觉盛宴。以巴黎的生命之河塞纳河为舞台，运动员们缓缓巡游，两岸码头为观众席，与古老的城市肌理和地标建筑交相辉映，在夕阳的金色光辉中，共同绘就一幅壮丽的画卷。
游行队伍将沿着塞纳河东向西行进6公里，始于巴黎植物园附近的奥斯特里茨桥，途经多个历史悠久的桥梁和标志性建筑，比如巴黎圣母院和卢浮宫。还有散落在城市中心的奥运场馆，包括荣军院和巴黎大皇宫。最终目的地则是位于埃菲尔铁塔对面的特罗卡德罗广场，官方将在那里继续进行仪式，然后点燃奥运会主火炬台，并宣布2024年巴黎奥运会正式开幕。
届时，组委会将动用近100艘船只，载着约1.05万名运动员沿着塞纳河巡游。对于参加巡游的206个国家和地区奥委会（NOC）来说，规模较大的NOC将拥有自己的船只，而规模较小的NOC将共用船只。
这种户外理念也使2024年巴黎奥运会开幕式成为观众规模和地理覆盖范围最大的一届。
巴黎奥组委官方表示，预计有近60万人能够亲临现场观看开幕式。除了下游码头的10.4万张付费门票外，还有22.2万张免费门票可供观众在塞纳河上游河岸观看巡游。历史上第一次，大多数观众无需支付门票即可观看开幕式，这也是2024年巴黎奥运会又一历史性里程碑。
除此之外，巴黎城还铺设了80个巨幕，那些没有抢到门票的人可以通过大屏幕观看开幕式。预计全球还将有15亿人通过电视转播收看开幕式。
席琳·迪翁有望亮相献唱
在巴黎奥运会开幕式的文艺汇演部分中，人们能够看到什么？虽然大多数的表演内容仍处于保密状态，但根据目前的线索，我们可以期待一场规模宏大、融合了古典与现代元素的精彩演出。
担任2024年巴黎奥运会和残奥会开闭幕式艺术总监的是法国戏剧导演兼演员托马斯·乔利。在一月份的媒体采访中，乔利表示希望将代表着法国不同文化的所有元素融合在一起，无论是歌剧还是歌唱，来展现法国多元的文化。“开幕式将把更加原汁原味的巴黎展示给观众。巴黎是一座永不疲倦的自我重构的城市，将多样性融汇到一起的那种感觉就是我们的目标，也是奥运会的目标。”他说。
据悉，届时多达400名舞者将参与演出，而参与2024年巴黎奥运会开闭幕式的演员总数将达到3000人。“奥运会开幕式将充分利用塞纳河沿岸的每一处历史遗迹。届时，每一段河岸，一座桥梁都将成为音乐、舞蹈和表演。”巴黎奥运会开闭幕式舞蹈总监和开幕式编舞负责人莫德·勒普拉戴克表示。“我们谈论的不仅仅是舞蹈，更将融入丰富的视觉画面、音乐和体育等元素，为观众呈现一场视听盛宴。”
随着开幕式的临近，参演歌手嘉宾的名单也进一步曝光。据外媒透露，以一首《我心永恒》被中国观众熟知的歌坛巨星席琳·迪翁有望在开幕式上演唱法国歌后艾迪特·皮亚芙的《爱的颂歌》。这也将是她自2022年宣布患上癌人综合征后，首次公开亮相。
这已经不是席琳·迪翁第一次参与奥运会的相关表演。1996年的亚特兰大奥运会开幕式上，她就演唱了名作《梦想的力量》。在全球观众面前拉开了奥运会的序幕。
除此之外，法国流行歌手米线将在共和国卫队的陪同下，演唱法国国宝级歌星夏尔·阿兹纳弗的经典歌曲，美国流行歌手Lady Gaga也将登上奥运舞台。
点燃主火炬的会是谁？
在开幕式当中，最终的主火炬如何点燃以及由谁来完成，当然是外界最为关注的话题。但这显然属于是巴黎奥运会开闭幕式的最高机密之一。
巴黎奥组委主席托尼·埃斯坦盖此前接受采访时甚至表示，将于本周五点燃奥运主火炬的人选甚至目前尚不知晓自己已被选中。有消息称，知道这一最终点火人选的人数不足十人。
目前，法国足球巨星齐达内是最被看好的人选。此前，他曾被认为是最早有可能在马赛迎接从希腊乘船而来的奥运圣火的人选，但最终并未参与其中。由此，人们猜测将有更为重要的任务等待他去完成。
法国足球运动员普拉蒂尼认为，最后这名点燃巴黎奥运会圣火的火炬手，必须是一位能够为全世界所公认的知名人物，且能够体现整个国家的精神。
作为法国最伟大的足球运动员，齐达内帮助法国队在1998年主场夺得首个世界杯冠军，他是法国人心目中的英雄。这位出身于马赛贫民区的球星，曾经给法国足球带来了无限荣耀。虽然齐达内也许并非完美之人，但他身上所蕴含的多元文化的象征性，与巴黎奥运会试图传递的内涵十分吻合。
当然，三届奥运田径金牌得主玛丽·何塞·佩雷克、著名演员奥马尔·锡、航天员托马斯·佩斯凯等人，也都是热门候选人。
善于制造惊喜的巴黎奥组委，是否会捧出另外一个意想不到的人选？让我们拭目以待！
Today, let us meet in the afterglow of the sunset over the Seine River.
The opening ceremony of the 2024 Paris Olympics will take place on the Seine River at 7:30 PM local time on October 26th and 1:30 AM on October 27th Beijing time. This will be the first time in the history of the Summer Olympics that the opening ceremony is held outside a stadium, echoing the Paris described by writer Ernest Hemingway as “a moving feast.” At the same time, it will be a vivid manifestation of the slogan “Olympic for All.”
So, what can we expect to see in this unprecedented Olympic opening ceremony?
Over 10,000 Athletes in the Parade
For the opening ceremony of the Paris Olympics, athletes will parade along the radiant Seine River, offering spectators a spectacular visual feast of the heart of Paris. The procession, following the course of the Seine, will weave through the city’s historical sites and architectural wonders. The Seine, the lifeblood of Paris, will serve as the stage for athletes to slowly cruise by, with the banks of the river as the seating for spectators, creating a stunning contrast with the ancient cityscape and iconic landmarks. Bathed in the golden light of the sunset, it will paint a magnificent picture.
The athletes will begin their journey along the Seine River, from the Austerlitz Bridge near the Jardin des Plantes, passing numerous historic bridges and landmarks such as Notre-Dame Cathedral, the Louvre, and Olympic venues like the Hôtel des Invalides and the Grand Palais. The procession will end at the Trocadero Plaza, directly opposite the Eiffel Tower, where the official ceremony will continue, including the lighting of the Olympic cauldron and the official declaration of the opening of the 2024 Paris Olympics.
Approximately 100 countries will be represented by about 10,500 athletes from 206 National Olympic Committees (NOCs). Larger NOCs will have their own boats, while smaller NOCs will share ones. This unique concept will make the 2024 Paris Olympics opening ceremony the largest in terms of both audience scale and geographical coverage.
The Paris organizing committee has stated that around 600,000 people are expected to witness the opening ceremony in person. In addition to the 60,000 paid tickets for the downstream quays, there will be 222,000 free tickets available for spectators along the upstream riverbanks. For the first time in history, spectators will be able to watch the opening ceremony without paying for tickets, marking another historic milestone for the 2024 Paris Olympics.
Moreover, 80 giant screens have been set up across Paris, allowing those without tickets to watch the opening ceremony on large screens.
Cultural Performances
What can we expect in the cultural performances during the opening ceremony? Although most of the performance details are still confidential, we can anticipate a grand show that combines both classical and modern elements.
The artistic director for the opening and closing ceremonies of the 2024 Paris Olympics and Paralympics is French theater director and actor Thomas Jolly. He has worked with James Jolly, who has blended all elements of French culture, from opera to rap, to showcase the country’s cultural diversity. “The opening ceremony will present a more authentic Paris to the audience. Paris is a city that never stops reinventing itself, and the fusion of diversity is our goal, as well as the goal of the Olympics,” he said.
It is reported that up to 400 dancers will participate in the performance, with a total of 3,000 performers in the opening and closing ceremonies. The performance will be divided into five acts, with each act representing a different aspect of Paris. Each stretch of the riverbank, each bridge will be filled with music, dance, and performances,” said Maël Le Plade, the dance director and choreographer of the opening ceremony. “We are not only talking about dance; it will also incorporate rich visual imagery, music, and sports, presenting an audiovisual feast for the audience.”
As the opening ceremony draws closer, the list of performing artists has begun to emerge. According to foreign media, Celine Dion, known as Chinese audiences for her song “My Heart Will Go On,” is expected to perform Edith Piaf’s “La Vie en Rose” during the ceremony. This will be her first public appearance since she announced in 2022 that she had been diagnosed with Stiff-Person Syndrome. This will not be Celine Dion’s first Olympic performance. In the 1996 Atlanta Olympics opening ceremony, she performed the iconic song “The Power of the Dream,” marking the beginning of the Olympic Games for audiences worldwide.
In addition to Celine Dion, French singer Noa Nahas, accordionist Jean-Michel Legrand, and Gould, will sing classic songs by French music legend Charles Aznavour. American pop star Lady Gaga is also expected to take the stage at the Olympics.
Who Will Light the Olympic Flame?
One of the most anticipated aspects of the opening ceremony is, of course, how and by whom the Olympic cauldron will be lit. This remains one of the biggest secrets of the Paris Olympics opening ceremony.
In an interview, Paris 2024 Organizing Committee President Tony Estanguet revealed that even the chosen person to light the Olympic cauldron might not yet know they have been selected. It is reported that fewer than ten people are aware of the identity of the final torchbearer.
Currently, French football legend Zinedine Zidane is considered the most likely candidate. He was previously thought to be the person who would receive the Olympic torch in Marseille, as it traveled from Greece by boat, but he did not participate in that event. This has led to speculation that he might have an even more significant task ahead.
Michel Platini, a famous French footballer, believes that the person chosen to light the Olympic flame should be a well-known figure globally, with a strong connection to France. “Zidane, as one of France’s greatest footballers, led the French national team to their first World Cup victory in 1998 and is a hero in the hearts of the French people. As a star from the working-class neighborhood of Marseille, Zidane brought glory to French football. Though not without flaws, his multicultural background resonates strongly with the core message of the Paris Olympics.
Other popular candidates include triple Olympic gold medalist Marie-José Pérec, renowned actor Omar Sy, and astronaut Thomas Pesquet.
As always, the Paris 2024 Organizing Committee is known for creating surprises. Will they unveil an unexpected torchbearer? Let’s wait and see!
H.3 Case Studies
Table 17 to 20 illustrate the multi-style outlines (concise vs. moderate vs. comprehensive and objective vs. literary) generated by Qwen2.5-3B with TKPO.

<table>
  <tr>
    <th>Article</th>
  </tr>
  <tr>
    <td>余春娇一度以为，张志明终于长大了。<br>她跟闺蜜说，两个人开始一起存钱买房。但是转身回家，春娇就见到志明抱回来一个在她看来一点用处都没有的“大拇指”，盛惠九万五。<br>“你知不知道每一个男人都需要买一个达利?”<br>张志明还是那个三十好几的幼稚鬼。但他硬是把无用之用编出花来，偏偏又让人觉得颇为趣致。<br>电影中春娇和志明住的房子里，摆放的大多是导演彭浩翔的收藏，这件达利雕塑“天使的幻想”想必也是。<br>像张志明和彭浩翔这种不甘寂寞又将无聊玩出新天际的潮人，达利作品无疑是最有吸引力的“高格调玩物”。—— 世界上最有自我营销的艺术大师没有之一<br>你可以说达利是个自恋狂，毕竟他声称在母亲的怀抱里就已经意识到自己是天才。可你不得不承认，他说的确实是一个事实。<br>如果说天才与疯子只有一步之遥，达利本人就是最好的体现。<br>哪怕大多数艺术家都有自己的小怪癖，但没有一个艺术家会像达利那么由内而外地一怪到底。他有一种与生俱来的恶作剧般的幽默感，不断地挑战和延展人们对荒诞艺术的理解。<br>后来，这种怪诞甚至成了有为之。达利不仅把离奇的梦境带进画布，用艺术诠释不疯魔不成魔的真谛，而且把自己也当作一个“艺术项目”来经营，成功将超现实主义带进主流生活。<br>论人设经营，达利可是第一人。他擅长上演足够吸引的表演，上扬的胡子、个性的装扮、口不择言的论调以及荒诞不羁的行为，都有一以贯之的超现实意味。<br>年仅30岁，达利已经登上了《时代周刊》的封面，他还办过自己的杂志 Dali News，简直是个初代自媒体人，还是最出圈的那一位。<br>达利的自我营销能力，恐怕让全世界的广告推销员心生妒忌，甚至让如今的自媒体人自叹弗如。<br>可以说，从头到脚，从作品到行头，Salvador Dali远远不止一个天才画家，这分明是一个故事精彩且足够完整的商业品牌。<br>比 KAWS 火一万倍的跨界艺术家才是潮人那杯茶<br>当代最火的艺术家，没有哪个人像达利那样如此放纵地表现自己。<br>他模仿委拉斯凯兹留胡子，并且始终如一地把它：他穿着深海潜水服在伦敦进行演讲，差点在演讲中途窒息；他用一万美金的价格把自己的一根胡子卖给小野洋子；安迪·沃霍尔送他一幅梦露，他把画放在地上，直接在画上尿尿……</td>
  </tr>
  <tr>
    <th>Concise</th>
    <th>Moderate&Objective</th>
    <th>Comprehensive</th>
    <th>Literary</th>
  </tr>
  <tr>
    <td>**目录**<br>1. **引言**<br>- 春娇与志明的日常生活与达利雕塑<br>- 达利作品的吸引力与“高格调玩物”<br>**目录**<br>1. 达利与张志明<br>2. 达利的自我营销<br>3. 达利的艺术与生活<br>4. 达利与跨界合作<br>2. **达利：艺术天才与疯子的边界**<br>3. **达利：天才与疯子的边界**<br>- 达利的自信与艺术突破<br>- 天才与疯子之间的微妙关系<br>4. **达利的艺术风格与“人设经营”**<br>- 达利的艺术风格与幽默感的体现<br>- 从疯魔到艺术的哲学探索<br>3. **达利的“艺术项目”：从天才到品牌**<br>- 自我营销的典范<br>- 达利的人设经营<br>- 成名与商业化：从画作到个人品牌<br>- 达利的公众形象与超现实主义风格<br>5. **达利的自我营销与媒体操作**<br>- 媒体曝光与《Dali News》的创办<br>- 达利的商业品牌与成功之道<br>6. **达利的离经叛道行为与当代影响**<br>- 达利的经典行为与文化互动<br>- 达利对当代艺术与文化的影响<br>7. **结语：达利的复杂性与隐喻**<br>- 达利作为艺术家的多面性<br>- 春娇与志明故事中的达利精神</td>
    <td>**目录**<br>1. **引言：余春娇与张志明的故事与期待**<br>- 春娇对志明的期待与现实的冲突<br>2. **张志明与达利雕塑的象征意义**<br>- 志明购入雕塑与春娇的反应<br>- 达利雕塑在家庭中的象征</td>
    <td>**目录**<br>1. **引言**<br>- 春娇与志明的日常生<br>活与达利雕塑<br>- 达利作品的吸引力与“高格调玩物”<br>- 达利的“艺术项目”：从天才到品牌**<br>- 自我营销的典范：达利的人设经营<br>- 成名与商业化：从画作到个人品牌<br>- 达利的商业品牌与成功之道<br>- 达利的经典行为与文化互动<br>- 达利对当代艺术与文化的影响<br>- 达利作为艺术家的多面性<br>- 春娇与志明故事中的达利精神</td>
    <td>**目录**<br>1. **引言**<br>- 春娇与志明的日常生<br>活与达利雕塑<br>- 达利作品的吸引力与“高格调玩物”<br>2. **达利：艺术天才与疯子的边界**<br>- 达利的自恋与天才气质<br>- 艺术怪癖与幽默感的体现<br>3. **达利的“艺术项目”：从天才到品牌**<br>- 自我营销的典范<br>- 达利的人设经营<br>- 成名与商业化：从画作到个人品牌<br>- 达利的商业品牌与成功之道<br>4. **总结**<br>- 达利的艺术成就与文化遗产<br>5. **结语**<br>- 达利的复杂性与隐喻**<br>- 达利作为艺术家的多面性<br>- 春娇与志明故事中的达利精神</td>
  </tr>
</table>

Table 17: Case study A.
Yu Chunjiao once thought that Zhang Zhiming had finally grown up.
She told her best friend that they had started saving money together to buy a house. But when she turned to go home, Chunjiao saw Zhiming come back with a “thumb” that, in her opinion, was completely useless, priced at 95,000 yuan.
"Do you know that every man needs to buy a Dali?"
Zhang Zhiming was still the same immature guy in his thirties. But he somehow managed to turn the useless into something with purpose, and oddly enough, it was somehow quite amusing.
In the exhibition titled "Dali's Self-Portrait," Chunjiao and Zhiming live mostly displays the collection of director Peng Haoxiang, and this Dali sculpture, "The Angel’s Fantasy", is likely one of them.
For people like Zhang Zhiming and Peng Haoxiang, who refuse to be bored and manage to turn dullness into something extraordinary, Dali’s works are undoubtedly the most attractive “high-class toys.”
I. The World’s Greatest Self-Marketing Artist, Without a Doubt
You could say Dali was a narcissist, since he claimed to have realized his genius in his mother’s arms. But you have to admit, he was indeed telling the truth.
If genius and madness are only a step apart, Dali was the perfect embodiment of this.
Though many artists have their own quirks, none of them were as wholly eccentric as Dali was, inside and out. He had an innate sense of mischievous humor, constantly challenging and expanding people’s understanding of absurdist art.
Later, this absurdity even became intentional. Dali not only brought bizarre dreams onto his canvases, using art to express the truth that one must go mad to be truly mad, but he also treated himself as an “art project,” successfully bringing surrealism into mainstream life.
When it comes to crafting a personal brand, Dali was the first. He was great at putting on a show that captivated attention: his upward-pointing mustache, eccentric outfits, outspoken opinions, and absurd behavior—all consistently carried a surreal meaning.
At just 30 years old, Dali was already on the cover of “Time” magazine. He even published his own magazine, “Dali News”, making him a pioneer of early self-media, and certainly one of the most viral figures of his time.
Dali’s ability to market himself was likely the envy of advertising professionals worldwide, and would probably make today’s self-media influencers feel inadequate.
From head to toe, from his art to his attire, Salvador Dali was far more than just a genius painter—he was a brand with a fascinating and complete story.
II. The Cross-Disciplinary Artist Who’s 10,000 Times More Popular Than KAWS Is the True Icon of the Trendy
No contemporary artist has ever expressed themselves so indulgently as Dali did.
He mimicked Velazquez by sporting a small mustache, and he consistently played around with it. He wore a deep-sea diving suit while giving a lecture in London, nearly suffocating during the talk. He sold a single hair from his mustache to Yoko Ono for $10,000. Andy Warhol gave him a portrait of Marilyn Monroe, and Dali laid it on the ground to urinate on it.

<table>
  <tr>
    <th>Concise</th>
    <th>Moderate & Objective</th>
    <th>Comprehensive</th>
    <th>Literary</th>
  </tr>
  <tr>
    <td colspan="4">**Outline**<br>1. **Introduction**<br>- The Daily Life of Chun-jiao and Zhi Ming and Dali’s Sculptures<br>- The Appeal of Dali’s Works and “High-Class Playthings”<br>**Outline**<br>1. Dali and Zhang Zhiming<br>2. Dali’s Self-Self-Marketing<br>3. Dali’s Art and Life<br>4. Dali and Cross-Industry Collaborations</td>
    <td colspan="2">**Outline:**<br>1. **Introduction**: The Story and Expectations of Yu Chun-jiao and Zhang Zhi-ming**<br>- The Conflict between Chun-jiao’s Expectations and the Reality of Zhi-ming<br>2. **The Symbolic Meaning of Zhang Zhi-ming and Dali’s Sculptures**<br>- Zhi-ming’s Purchase of the Sculpture and Chun-jiao’s Reaction<br>- The Symbolism of Dali’s Sculpture in the Family<br>3. **Dali: The Boundary between Genius and Madness**<br>- Dali’s Confidence and Artistic Breakthroughs<br>- The Subtle Relationship between Genius and Madness<br>4. **Dali’s Artistic Style and “Personal Branding”**<br>- Dali’s Artistic Style and Sense of Humor<br>- How Dali Shaped His Public Image and Surrealism in Everyday Life<br>5. **Dali’s Self-Marketing and Media Manipulation**<br>- Media Exposure and the Founding of “Dali News”<br>- Dali’s Commercial Branding and Path to Success<br>6. **Dali’s Iconoclastic Behavior and Contemporary Influence**<br>- Dali’s Iconic Actions and Cultural Interactions<br>- Dali’s Impact on Contemporary Art and Culture<br>7. **Conclusion**: The Complexity and Metaphor of Dali**<br>- The Multifaceted Nature of Dali as an Artist<br>- The Dali Spirit in the Story of Chun-jiao and Zhi-ming</td>
    <td>**Table of Contents**<br>1. **Introduction**<br>- The Daily Life of Spring Jiao and Zhimin and Dali’s Sculptures<br>- The Appeal of Dali’s Works and “High-Class Toys”<br>2. **Dali: The Border Between Artistic Genius and Madness**<br>- Dali’s Narcissism and Genius Temperament<br>- Artistic Eccentricities and the Manifestation of Humor<br>- From Madness to Philosophical Expression in Art<br>3. **Dali’s “Artistic Project”: From Genius to Brand**<br>- A Model of Self-Marketing: Dali’s Personal Brand Management<br>- Fame and Commercialization: From Paintings to Personal Brand<br>- Dali’s Public Image and Surrealist Style<br>4. **Conclusion**<br>- Dali’s Artistic Achievements and Cultural Legacy<br>- From Art to Lifestyle: Dali’s Impact on His Era</td>
  </tr>
</table>

Table 18: Case study A. (Translation)
好剧推荐之《金田一少年事件簿》：“谁杀了谁就能得到救赎，我一个也没见过。”
今天推荐一部动漫《金田一少年事件簿》，由日本推理漫画改编。本片的主角金田一虽然说是全日本最白痴的高中生之一，上课老在睡觉，作业从来都是抄青梅竹马美雪的，什么事情都由美雪帮他扛着。然而，少年金田一却有着遗传自爷爷的优秀基因——智商180。面对匪夷所思、杀机四伏的疑案，身临险境之中也能察觉到每个蛛丝马迹，抓住解谜的关键——破解层出不穷的巧妙杀人手段。本片中每一个事件几乎没有联系，都是独立的个案，不存在跳过就看不懂的情况。金田一里大多是杀人事件，且是连环杀人事件，场景布置也带有阴森诡谲的风格。给整个案子提供了恐怖的氛围。其中一篇《歌剧院新杀人事件》非常经典。案件发生在静冈县歌岛上，自从发生过轰动全国的连续杀人事件后，古老歌剧院被拆毁了，并在原址兴建了一个新的剧场。为了庆祝新剧院的落成，“幻想剧团”打算举行纪念性的公演，还决定在歌岛上排练话剧《歌剧院魅影》。金田一、美雪以及剑持警部受到岛主黑泽先生的邀请，一起前往孤岛观赏幻想剧团的排练。可是当众人抵达歌岛后，歌岛的外界来往的游艇却出了问题，众人因此被困在歌岛上，歌岛成了天大的密室。这时，一名自称“幽灵”的凶手，开始根据《歌剧院魅影》的剧本，逐一杀害幻想剧团的成员。引起这个案件的种子就是美歌——一个上吊自杀的悲剧女歌剧演员。这个名字于团队而言也是诅咒般的存在。《歌剧院魅影》本身即是可怕而悲惨的故事。剧院的怪人有着上天赋予的音乐才能，长相却极其丑陋。他深深的爱着美丽的剧院歌手，但是由于得不到歌手的爱，怪人因爱生恨犯下残忍的案件。而这次的案件也像剧本般如出一辙的上演了。美歌作为歌剧院中美丽并且有实力的女歌剧者，遭来了他人的嫉妒，被下致幻药，在舞台上泣血百出，并遭到歌剧院管理者的侵犯，还拍下了录像。美歌在喝下致幻药后误认为她的爱人能条背叛了她。在舞台上自杀。在她死后，能条为了替她报仇，精心设计了复仇计划，将所有伤害她的人一一杀掉。因为爱而生恨，因为恨而犯下大错。歌剧院的舞台突然上演恐怖一幕——巨大的吊灯压在日高绘绘的身上，一切就像歌剧一样出现在众人眼前。而这正是美歌死前的诅咒：化身歌剧院的怪人。SPRING FESTIVAL 每一个故事都藏着一个悲剧的内核，探案的背后是许许多多的爱恨情仇和无尽的复仇。在惊恐过后，更多的感情与体会浮现出来，值得我们认真咀嚼。如果有喜欢推理的小伙伴，可以看看这部经典的推理动漫，或许会有出乎意料的收获。

<table>
  <tr>
    <th>Concise</th>
    <th>Moderate&Objective</th>
    <th>Comprehensive</th>
    <th>Literary</th>
  </tr>
  <tr>
    <td>**目录**<br>1. **推荐介绍**<br>**推荐作品**:《金田一少年事件簿》<br>**推荐原因**:由日本推理漫画改编，具有连环杀人事件和阴森诡异的风格。<br>2. **主角介绍**<br>**金田一**:全日本最白痴的高中生之一，智商180，擅长解谜。<br>**美雪**:金田一的青梅竹马，经常帮助金田一。<br>3. **剧情概述**<br>《金田一少年事件簿》动漫推荐：主要角色**:金田一、美雪、剑持警部等。<br>1. 主角：金田一<br>2. 《歌剧院新杀人事件》经典案例<br>**《歌剧院新杀人事件》**:背景：静冈县歌岛歌剧院重建后的幻想剧团。<br>**凶手**:幽灵，根据《歌剧院魅影》剧本逐步杀害成员。<br>**动机**:美歌自杀事件引起的嫉妒和报复心理。<br>5. **情感与反思**<br>**悲剧内核**:探索背后隐藏着爱情恨和复仇。<br>**感悟**:在惊恐过后，更深层次的情感与思考。<br>6. **推荐理由**<br>**适合人群**:喜欢推理的小伙伴。</td>
    <td>目录<br>一、引言<br>1. **动漫推荐**<br>-《金田一少年事件簿》<br>-改编自日本推理漫画<br>2. **主角介绍**<br>-金田一的性格特点<br>-美雪的角色与作用<br>2. **《金田一少年事件簿》概述**<br>1. **故事情节**<br>-发生地点与时间<br>-故事的独立性：每个案件的独立性与非关联性<br>2. **金田一的特点与侦探能力**<br>-智商与推理天赋<br>-应对疑案的技巧与方法<br>3. **案件分析**<br>-主要案件类型：连环杀人事件<br>-案件的恐怖氛围与谜题设计<br>三、经典案例分析：《歌剧院新杀人事件》<br>1. **案件背景**<br>发生地点：静冈县歌岛<br>“幻想剧团”的公演与排练<br>2. **案件起因**<br>美歌的自杀与其他悲剧经历<br>《歌剧院魅影》剧本与案件的联系<br>3. **案件发展**<br>众人被困歌岛<br>“幽灵”凶手的出现与杀人手法<br>4. **案件解析**<br>-美歌的悲剧与复仇计划<br>-巨大吊灯事故与死亡事件的重演<br>四、案件背后的情感与主题<br>1. **悲剧的内核**<br>-爱与恨交织的动机<br>-复仇与情感的纠葛<br>2. **推理与人性的碰撞**<br>-对情感与复仇的深刻剖析<br>-人物情感的多重解读<br>五、总结与推荐<br>1. **总结**<br>-《金田一少年事件簿》的魅力所在<br>-推理和人性探索的结合</td>
    <td>目录<br>一、引言<br>1. **动漫推荐**<br>-《金田一少年事件簿》<br>-改编自日本推理漫画<br>2. **主角介绍**<br>-金田一的性格特点<br>-美雪的角色与作用<br>2. **《金田一少年事件簿》概述**<br>1. **故事情节**<br>-发生地点与时间<br>-故事的独立性：每个案件的独立性与非关联性<br>2. **金田一的特点与侦探能力**<br>-智商与推理天赋<br>-应对疑案的技巧与方法<br>3. **案件分析**<br>-主要案件类型：连环杀人事件<br>-案件的恐怖氛围与谜题设计<br>三、经典案例分析：《歌剧院新杀人事件》<br>1. **案件背景**<br>发生地点：静冈县歌岛<br>“幻想剧团”的公演与排练<br>2. **案件起因**<br>美歌的自杀与其他悲剧经历<br>《歌剧院魅影》剧本与案件的联系<br>3. **案件发展**<br>众人被困歌岛<br>“幽灵”凶手的出现与杀人手法<br>4. **案件解析**<br>-美歌的悲剧与复仇计划<br>-巨大吊灯事故与死亡事件的重演<br>四、案件背后的情感与主题<br>1. **悲剧的内核**<br>-爱与恨交织的动机<br>-复仇与情感的纠葛<br>2. **推理与人性的碰撞**<br>-对情感与复仇的深刻剖析<br>-人物情感的多重解读<br>五、总结与推荐<br>1. **总结**<br>-《金田一少年事件簿》的魅力所在<br>-推理和人性探索的结合</td>
    <td>目录<br>一、引言<br>1. **动漫推荐**<br>-《金田一少年事件簿》<br>-改编自日本推理漫画<br>2. **主角介绍**<br>-金田一的性格特点<br>-美雪的角色与作用<br>2. **《金田一少年事件簿》概述**<br>1. **故事情节**<br>-发生地点与时间<br>-故事的独立性：每个案件的独立性与非关联性<br>2. **金田一的特点与侦探能力**<br>-智商与推理天赋<br>-应对疑案的技巧与方法<br>3. **案件分析**<br>-主要案件类型：连环杀人事件<br>-案件的恐怖氛围与谜题设计<br>三、经典案例分析：《歌剧院新杀人事件》<br>1. **案件背景**<br>发生地点：静冈县歌岛<br>“幻想剧团”的公演与排练<br>2. **案件起因**<br>美歌的自杀与其他悲剧经历<br>《歌剧院魅影》剧本与案件的联系<br>3. **案件发展**<br>众人被困歌岛<br>“幽灵”凶手的出现与杀人手法<br>4. **案件解析**<br>-美歌的悲剧与复仇计划<br>-巨大吊灯事故与死亡事件的重演<br>四、案件背后的情感与主题<br>1. **悲剧的内核**<br>-爱与恨交织的动机<br>-复仇与情感的纠葛<br>2. **推理与人性的碰撞**<br>-对情感与复仇的深刻剖析<br>-人物情感的多重解读<br>五、总结与推荐<br>1. **总结**<br>-《金田一少年事件簿》的魅力所在<br>-推理和人性探索的结合</td>
  </tr>
</table>

Table 19: Case study B.
Recommended Drama: The Case Files of Young Kindaichi* "Whoever kills someone can gain redemption. I haven't seen a single one yet."
Today, I recommend an anime, The Case Files of Young Kindaichi, based on a Japanese mystery manga. The protagonist, Kindaichi, is often considered one of the most clueless high school students in all of Japan. He sleeps in class, always copies his childhood friend Miyuki’s homework, and relies on her to shoulder all his burdens. However, young Kindaichi has inherited an exceptional gene from his grandfather—an IQ of 180. Faced with baffling and life-threatening murder cases, he can detect even the slightest clue, unravel key mysteries, and decipher the ever-evolving, ingenious methods of murder. Each episode in this series is a new case, and the viewer is never sure whether they will lose it if they skip one. Most cases involve murders, often serial killings, set against eerie, suspenseful backdrops, creating a chilling atmosphere for the cases.
One classic episode is The New Murder Case at the Opera House. The case takes place on Shizuoka Prefecture’s Ōkage Island, where a string of shocking murders occurred years ago. The old opera house was torn down, and a new theater was built on the same site. To celebrate the theatre’s grand opening, the Fantasy Theatre staged a commemorative performance. Kindaichi chose to reenact the play The Phantom of the Opera on the island. Kindaichi, Miyuki, and Inspector Kenmochi are invited by the island’s owner, Mr. Kuroda, to watch the troupe perform. Halfway through the play, they arrive on the island, the yacht that was supposed to transport them back to the mainland breaks down, trapping them on the island, turning it into a natural locked room. At that point, a murderer, claiming to be a “ghost,” begins to kill the troupe members one by one, following the plot of The Phantom of the Opera.
The seed of this case was planted years earlier with the tragic death of a singer named Miga, who committed suicide by hanging. For the troupe, her name is like a curse. The Phantom of the Opera itself is a haunting, tragic story. The opera’s “phantoms” possesses divine musical talent but is grotesquely disfigured. He despises lovers, because opera singers are expected to be hermaphrodites, latched and crouchy, and the terrible crimes are part of love-tempted jealousy. This new case eerily mirrors that story. Miga, a talented and beautiful singer, became the target of envy. She was introduced with a hallucinogen, behaved erratically on stage, and was assaulted by the theatre’s manager, who even filmed the attack. Under the influence of the drug, Miga believed her lover had betrayed her and ended her life on stage. After her death, her lover meticulously planned a revenge scheme, killing everyone who had wronged her. What began as love turned to hate, and hatred led to tragic actions.
The opera house stage suddenly erupts into a terrifying scene when a huge chandelier crashes down onto the actress Hitomi Shiori, much like a scene from the opera itself. The murderer Mika hides behind her, watching the rest of the opera house. Each story in SPRING FESTIVAL contains a core of tragedy, and behind each investigation lies a complex web of love, hate, and endless revenge. After the terror subsides, deeper emotions and insights emerge, giving us much to reflect on. If you enjoy mystery and detective stories, this classic anime might offer some unexpected discoveries.

<table>
  <tr>
    <th>Concise</th>
    <th>Moderate&Objective</th>
    <th>Comprehensive</th>
    <th>Literary</th>
  </tr>
  <tr>
    <td colspan="4"><b>*Outline*</b></td>
  </tr>
  <tr>
    <td colspan="4">1. **Introduction to the Recommendation**</td>
  </tr>
  <tr>
    <td colspan="4">2. **Recommended Work**: The Case Files of Young Kindaichi</td>
  </tr>
  <tr>
    <td colspan="4">- **Reason for Recommendation**: Adapted from a Japanese mystery manga, featuring serial murders and a dark, eerie atmosphere.</td>
  </tr>
  <tr>
    <td colspan="4">2. **Introduction to the Main Characters**</td>
  </tr>
  <tr>
    <td colspan="4">- **Kindaichi**: One of the most clueless high school students in Japan, with an IQ of 180, skilled in solving mysteries.</td>
  </tr>
  <tr>
    <td colspan="4">- **Miyuki**: Kindaichi’s childhood friend, who frequently assists him.</td>
  </tr>
  <tr>
    <td colspan="4">3. **Plot Overview**</td>
  </tr>
  <tr>
    <td colspan="4">**Case Background**: The case occurs on Ōtate Island in Shizuoka Prefecture, centered around an opera house.</td>
  </tr>
  <tr>
    <td colspan="4">**Main Characters**: Kindaichi, Miyuki, Inspector Kenmochi, and others.</td>
  </tr>
  <tr>
    <td colspan="4">**Core of the Case**: A series of murders triggered by the suicide of Miga.</td>
  </tr>
  <tr>
    <td colspan="4">4. **Analysis of Classic Cases**</td>
  </tr>
  <tr>
    <td colspan="4">**"The New Murder at the Opera House"**: A commemorative performance in the newly renovated opera house in Shizuoka Prefecture.</td>
  </tr>
  <tr>
    <td colspan="4">**Murderers**: The “Ghost,” who gradually kills the members according to the script of The Phantom of the Opera.</td>
  </tr>
  <tr>
    <td colspan="4">- **Motives**: Jealousy and revenge stemming from Miga’s suicide.</td>
  </tr>
  <tr>
    <td colspan="4">5. **Emotions and Reflections**</td>
  </tr>
  <tr>
    <td colspan="4">**The Tragic Core**: Behind the investigation lies love, hatred, and vengeance.</td>
  </tr>
  <tr>
    <td colspan="4">**Insight**: After the fear subsides, deeper emotions and thoughts emerge.</td>
  </tr>
  <tr>
    <td colspan="4">6. **Conclusion & Recommendations**</td>
  </tr>
  <tr>
    <td colspan="4">**Target Audience**: Fans of mystery and detective stories.</td>
  </tr>
  <tr>
    <td colspan="4"><b>*Outline*</b></td>
  </tr>
  <tr>
    <td colspan="4">1. **Introduction**</td>
  </tr>
  <tr>
    <td colspan="4">2. **Anime Recommendations**</td>
  </tr>
  <tr>
    <td colspan="4">- The Case Files of Young Kindaichi</td>
  </tr>
  <tr>
    <td colspan="4">- Adapted from a Japanese mystery manga</td>
  </tr>
  <tr>
    <td colspan="4">2. **Main Character Introductions**</td>
  </tr>
  <tr>
    <td colspan="4">- Personality traits of Kindaichi</td>
  </tr>
  <tr>
    <td colspan="4">- Relationship of Miyuki</td>
  </tr>
  <tr>
    <td colspan="4">**II. Overview of The Case Files of Young Kindaichi** 1. **Story Background**</td>
  </tr>
  <tr>
    <td colspan="4">- Setting: Locations and Time Period</td>
  </tr>
  <tr>
    <td colspan="4">- Independence of Each Case: The Standalone Nature of Each Mystery</td>
  </tr>
  <tr>
    <td colspan="4">2. **Kindaichi’s Characteristics and Detective Skills**</td>
  </tr>
  <tr>
    <td colspan="4">- Intelligence and Deductive Talent</td>
  </tr>
  <tr>
    <td colspan="4">- Techniques and Methods for Solving Mysteries</td>
  </tr>
  <tr>
    <td colspan="4">3. **Case Style**</td>
  </tr>
  <tr>
    <td colspan="4">- Primary Case Types: Serial Murders</td>
  </tr>
  <tr>
    <td colspan="4">- The Horrific Atmosphere and Puzzle Design of the Cases</td>
  </tr>
  <tr>
    <td colspan="4">**III. Classic Case Analysis: The Opera House Murder Case**</td>
  </tr>
  <tr>
    <td colspan="4">1. **Case Background**</td>
  </tr>
  <tr>
    <td colspan="4">- Location: Shizuoka Prefecture, Utagashima</td>
  </tr>
  <tr>
    <td colspan="4">- Portion of Rehearsals of the “Phantom Troupe”</td>
  </tr>
  <tr>
    <td colspan="4">2. **Incident Cause**</td>
  </tr>
  <tr>
    <td colspan="4">- The Suicide of Miku and Her Tragic Past</td>
  </tr>
  <tr>
    <td colspan="4">- Connection Between The Phantom of the Opera Script and the Case</td>
  </tr>
  <tr>
    <td colspan="4">3. **Case Development**</td>
  </tr>
  <tr>
    <td colspan="4">- The Group Is Trapped on Utagashima</td>
  </tr>
  <tr>
    <td colspan="4">- The “Ghost” Killer’s Appearance and Murder Methods</td>
  </tr>
  <tr>
    <td colspan="4">4. **Case Analysis**</td>
  </tr>
  <tr>
    <td colspan="4">- Miku’s Tragedy and Her Revenge Plan</td>
  </tr>
  <tr>
    <td colspan="4">- The Grand Chandelier Accident and the Reenactment of the Fate Accident</td>
  </tr>
  <tr>
    <td colspan="4">**IV. Emotions and Themes Behind the Case**</td>
  </tr>
  <tr>
    <td colspan="4">1. **The Tragic Core**</td>
  </tr>
  <tr>
    <td colspan="4">- The Motive of Love and Hate Intertwined</td>
  </tr>
  <tr>
    <td colspan="4">- The Intersection of Revenge and Emotions</td>
  </tr>
  <tr>
    <td colspan="4">2. **The Collision of Reason and Human Nature**</td>
  </tr>
  <tr>
    <td colspan="4">- A Deeper Look at Emotions and Revenge</td>
  </tr>
  <tr>
    <td colspan="4">- Multiple Interpretations of Character Emotions</td>
  </tr>
  <tr>
    <td colspan="4">**V. Conclusion and Recommendations**</td>
  </tr>
  <tr>
    <td colspan="4">1. **Conclusion**</td>
  </tr>
  <tr>
    <td colspan="4">- The Appeal of The Case Files of Young Kindaichi</td>
  </tr>
  <tr>
    <td colspan="4">- The Fusion of Mystery and Human Nature Exploration</td>
  </tr>
</table>

Table 20: Case study B. (Translation)

Reviewer paper summaries: The paper proposes TKPO, a token-level preference self-alignment method for multi-style outline generation. It addresses limitations of existing preference alignment approaches (e.g., reliance on response pairs, high computational costs) by extending the Bradley-Terry model to list-wise token-level comparisons and using attribute-driven rejection sampling.

This paper introduces a novel task: multi-style controllable outline generation, focusing on language style (literary vs. objective) and level of detail (concise vs. moderate vs. comprehensive). The authors propose a Token-level Preference Self-Alignment Optimization (TKPO) method to tackle this. TKPO extends the Bradley-Terry model to a listwise comparison at the token level and employs a controllable attribute-driven approach to generate reject samples, thus circumventing the need for paired responses. The authors curate two new datasets for this task and demonstrate through experiments that TKPO outperforms DPO in terms of both performance and training efficiency.

This paper presents an innovative and efficient token-level preference alignment method (TKPO) and demonstrates its superiority in multi-style outline generation tasks. Through extensive experiments and theoretical analysis, TKPO demonstrates superior performance and efficiency compared to existing methods, offering a promising solution for precise and efficient controllable text generation.
Summary of strengths: 1. The paper is well written and easy to follow.
2. They curate two datasets (language style and level-of-detail) with professional annotations, enabling community benchmarking.
3. Designs style-specific metrics (e.g., Outline Token Entropy) and provides comprehensive efficiency/effectiveness analyses, setting a strong empirical foundation for future work.

- The paper addresses a novel and potentially valuable task: multi-style controllable outline generation.
- The proposed TKPO method is technically interesting, particularly its extension of the Bradley-Terry model to the token level and the self-alignment strategy.
- The method's ability to function without requiring paired response data is a significant practical advantage.
- Experimental results show promising performance and training efficiency improvements compared to the DPO baseline.
- Creating a new dataset specifically for this task is a valuable contribution to the community.

1. The TKPO method introduces token-level preference alignment, eliminating the need for paired response data while improving generation diversity and fine-grained control；
2. The method significantly reduces training time and memory usage compared to existing methods.
Summary of weaknesses: 1.	The task proposed in this paper is very similar to the purpose of personalized alignment, and there has been much related work on personalized alignment [1,2,3…….], but the authors did not mention these works.
2.	Directly comparing efficiency with DPO is unfair. This paper merely borrows the approach from other works by removing the reference model, which can hardly be considered a contribution of this paper. On the contrary, since this method requires additional rejected token selection and necessitates the generation of prompts with anti-attributes, the efficiency of this part is overlooked by the authors.
3.	Is the method transferable across different attributes? For instance, if it is trained using the attribute 'concise' but tested with its synonym 'detailed', would it still work?
4.	This paper assumes that the user-specified attributes are binary and polarized, and thus the method does not support attributes such as "slightly more concise."
5.	Moreover, the combination of attributes, such as "objective and concise," has not been taken into consideration.
		
[1] Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging

[2] Arithmetic control of llms for diverse user preferences: Directional preference alignment with multi-objective rewards

[3] MaxMin-RLHF: Alignment with Diverse Human Preferences

- The novelty regarding token-level optimization might be somewhat limited, as existing work has explored this direction.
- Concerns exist regarding the dataset's characteristics, including its likely single-language nature (Chinese), potential lack of representativeness due to its origin from a specific social media platform, and the specifics of the labeling process, especially the use of GPT-4o for generating objective style outlines.
- The rationale behind the design of the language style taxonomy (literary vs. objective) requires further justification, particularly given the potential overlap between these categories.
- The paper lacks a thorough discussion on the broader applicability of the proposed listwise approach and a direct comparison with a pairwise baseline derived from the listwise data to demonstrate the necessity of the listwise modeling.

1. The paper relies heavily on automatic metrics (e.g., BLEU, ROUGE), but lacks human evaluation to assess subjective aspects like style consistency and readability
2. The evaluation is restricted to Chinese datasets, and the tasks are limited to news and magazine articles, raising questions about the generalizability of TKPO to other languages and domains.
Comments, suggestions and typos: see above

Recommendations:
- Refine the novelty claim in the abstract and introduction to reflect the specific contributions of TKPO more accurately.
Provide comprehensive details about the curated datasets, including a discussion on the representativeness of the data source, and a thorough explanation of the generation process using GPT-4o, including any quality control measures or underlying assumptions.
- Expand the discussion on the design choices for the language style taxonomy, justifying the selection of "literary" and "objective" and acknowledging potential limitations.
- Include a more detailed discussion on the generalizability of the listwise approach and conduct a comparative experiment with a pairwise baseline derived from the same data to demonstrate the effectiveness and necessity of the listwise modeling in TKPO.

Questions for the Authors:
- What measures were taken to ensure the quality and objectivity of the outlines generated by GPT-4o? What were the underlying assumptions for using GPT-4o as a reference for objective style?
- Could you elaborate on the specific criteria and considerations used to define the "literary" and "objective" language styles?
What are your thoughts on the broader applicability of the token-level listwise Bradley-Terry approach to other controllable generation tasks?
- Would it be feasible to include a comparative experiment where the listwise training data is broken down into pairwise data to train a baseline model, allowing for a direct evaluation of the benefits of the listwise approach?

1. The current dataset is limited to Chinese, I would like to know the effect on English, whether there are any experiments.
2. The language style classification (literary vs. objective) is relatively simple. Whether other attributes can be controlled, such as  sentence structure, part of speech(noun, verb).
Overall assessment scores: 3

3.5

3
Metareview score: 3.5
Metareview: This paper introduces a novel token-level preference alignment method (TKPO) designed for multi-style controllable outline generation. TKPO innovatively extends the Bradley-Terry model to perform listwise token-level comparisons and uses controllable attribute-driven rejection sampling to eliminate the need for paired responses. The proposed method addresses key limitations in existing alignment techniques, such as high computational cost and inefficiency in multi-style generation settings. The authors also create two new datasets and demonstrate that TKPO outperforms DPO in both alignment quality and training efficiency, making it a promising solution for fine-grained, controllable text generation tasks.
Summary of reasons to publish: - Proposes a novel token-level preference alignment method (TKPO) that extends the Bradley-Terry model and removes the need for paired response data
- Demonstrates strong empirical performance and substantial efficiency gains over DPO, with reduced training time and memory usage
- Introduces a valuable new task—multi-style controllable outline generation—along with two professionally annotated datasets and style-specific evaluation metrics