Discrimination for Generation

## Abstract

Abstract. Artificial Intelligence Generated Content (AIGC) technology development has facilitated the creation of rumors with misinformation, impacting societal, economic, and political ecosystems, challenging democracy. Current rumor detection efforts fall short by merely labeling potentially misinformation (classification task), inadequately addressing the issue, and it is unrealistic to have authoritative institutions debunk every piece of information on social media. Our proposed comprehensive debunking process not only detects rumors but also provides explanatory generated content to refute the authenticity of the information. The Expert-Citizen Collective Wisdom (ECCW) module we designed aensures high-precision assessment of the credibility of information and the retrieval module is responsible for retrieving relevant knowledge from a Real-time updated debunking database based on information keywords. By using prompt engineering techniques, we feed results and knowledge into a LLM (Large Language Model), achieving satisfactory discrimination and explanatory effects while eliminating the need for fine-tuning, saving computational costs, and contributing to debunking efforts.

## 1. Introduction

Figure: Figure 1. Example of rumor. Both in the academic and industrial sectors, only the first half of the work has been done so far. However, for users influenced by information, this is not sufficient to convince them. Therefore, the second half of the work is what reality demands.
Refer to caption: extracted/2403.20204v1/img/2.png

Social media platforms such as Weibo(^1^11https://m.weibo.cn) and Twitter(^2^22 https://twitter.com) have become increasingly popular for users to share information, express their opinions, and keep up with current events (Pham et al., ). However, these platforms have also been exploited by some individuals to spread rumors. Rumors are defined as widely spread stories or statements that have not been verified or confirmed for their truthfulness (Yang et al., ). Given the sheer number of users on social media and the ease with which information can be accessed and shared, unverified reports and fabricated rumors can spread quickly, potentially causing widespread confusion and panic within society, and even having economic and political consequences (Bi et al., ). In order to address this issue, putting an end to rumors on social media is crucial to mitigate their potential negative impact.

In the past, the primary focus of rumor research centered around rumor classification (Dou et al., ; Yang et al., ; Zheng et al., ) and rumor verification (You et al., ). While these studies yielded promising results on their respective datasets, the models developed therein were predominantly designed as single-task systems, specializing in tasks such as classification and regression. However, these models often fell short of fully leveraging the potential of language, and they lacked the expressive and interactive capabilities highlighted in recent research
(Singhal et al., ). As a result, a notable disparity exists between the current capabilities of these models and the expectations placed on them within real-world rumor debunking workflows (See Figure 1). A mature rumor debunking system requires diverse capabilities. First, it needs to accurately comprehend the complexity of human language (Cui et al., ; Nguyen et al., ), including semantics, logic, and emotions. Only such a system can make precise judgments when faced with various forms of rumors and identify any loopholes or contradictions (Lin et al., ). Secondly, a successful rumor debunking system should integrate a vast amount of existing knowledge and information to enhance its ability to discern the authenticity of rumors (Cui et al., ). This requires the system to gather data from multiple sources and carry out effective information integration and reasoning to form comprehensive and reliable rumor debunking results (Sun et al., ). Furthermore, the rumor debunking system should be interactive, allowing real-time communication and interaction with users (Singhal et al., ). In practical applications, the rumor debunking system must also consider the timeliness and reliability of information (Zhu et al., ). As rumors can spread rapidly on social media, providing timely feedback and responses are crucial to prevent further dissemination of misinformation.

With the rise of large language models and the successful applications like ChatGPT(^3^33https://chat.openai.com/) , new possibilities have emerged for the development of automated and interactive rumor debunking systems. The significance of large language models in the field of natural language processing cannot be underestimated. Their powerful representation learning abilities enable them to grasp the complexities and semantic information of language from extensive corpora (Vaswani et al., ). As a result, these models provide a robust foundation for the construction of efficient rumor debunking systems.The paper presents contributions concerning the integration of rumor debunking systems with large language models. Our contributions are outlined below:

- •
To the best of our knowledge, we are the first to develop a comprehensive rumor debunking system using a large language model (involving retrieval, discrimination, and guided generation).
- •
We proposed ECCW and evaluated its discriminative performance against rumors from various domains. The results show that our model, by encoding textual information and feeding it into two parallel subnetworks, achieves the optimal discriminative effect.
- •
We have transformed existing publicly debunked content into a searchable knowledge vector database. This database can be utilized for keyword-based retrieval of information related to relevant rumor queries, facilitating the provision of the reasoning and analytical knowledge required for practical debunking applications.
- •
We integrate the discriminative results from ECCW with keyword information retrieved from a vector database through the prompt engineering process. Utilizing the Retrieval Augmented Generation, we have successfully produced persuasive information that debunks rumors.

## 2. Related Work

Before the advent of transformer, some rumor detection efforts primarily relied on statistical methods (Xu et al., ). However, in the era of Large Language Models (LLMs), these methods have become almost ineffective, as LLMs like GPT can be exploited to generate rumors for sensationalism. The research indicated that, compared to rumors containing misinformation crafted by humans, those generated by LLMs are more challenging for both humans and detectors to identify (Ji et al., ). This suggests that they may exhibit a more deceptive style and potentially cause greater harm (Chen and Shu, ).At the same time, the emergence of LLMs holds tremendous potential for reshaping the landscape in combating misinformation. Their extensive and diverse global knowledge, coupled with robust reasoning capabilities, presents a promising opportunity to counteract misinformation.
Didem et al. () studied the use of large language models (LLMs) to explore the interaction between machine-generated and human-written real and fake news articles. They found that detectors trained specifically on human-written articles were indeed good at detecting machine-generated fake news, but not vice versa. Pavlyshenko () explored the possibility of using the Llama 2 large language model (LLM) based on PEFT/LoRA fine-tuning for fake news detection and information distortion analysis. Chen et al. () investigated the understanding capabilities of multiple LLMs regarding both content and propagation across social media platforms. They designed four instruction-tuned strategies to enhance LLMs for both content and propagation-based misinformation detection, resulting in better detection performance.

The detection task is merely one component of the comprehensive debunking process; it is essential to include a fact-checking step to assist users in comprehending inaccuracies within the information. A deep-learning-based fact-checking URL recommender system was suggested with the aim of offering dependable information sources for inquiring users (You et al., ). Nevertheless, a drawback of this approach is that the majority of users prefer content that is direct and explanatory, rather than having to open websites and manually search for relevant evidence. Therefore, utilizing the generative capability of LLMs to produce explanatory content for information is a reasonable direction. However, the hallucination issues of LLMs may result in the generated explanatory content containing a significant amount of misinformation (Chen and Shu, ), turning the inquirer into a secondary victim of rumors. In order to reduce the problem of hallucinations caused by the LLM model, Wei et al. () employed a Chain of Thought (CoT) prompt to induce more faithful reasoning. Jiang et al. () used knowledge graphs, datasheets, and structured databases as external knowledge bases to improve LLM reasoning. Tian et al. () pointed out that utilizing Knowledge Graphs (KGs) to enhance language modeling through joint training and custom model architectures incurs significant costs. Retrieval-augmented generation, however, could be a better solution. They proposed Graph Neural Prompting, which demonstrated advantages in commonsense and reasoning tasks across various scales and configurations of LLMs.

## 3. Method

Figure: Figure 2. The comprehensive framework for debunking processes.In the bottom-left corner, it represents the user’s inquiries about the authenticity of the rumors they need to verify. In the top-left corner lies the process of constructing a database of debunking knowledge vectors. The bottom-right corner depicts the simplified workflow of our rumor discrimination network. The top-right corner illustrates how to obtain interpretable debunking content by integrating the debunking results with the retrieved knowledge through RAG (Retrieval-Augmented Generation).
Refer to caption: extracted/2403.20204v1/img/frame.png

In this section, we will introduce the debunking process we proposed and provide a detailed explanation of its components and design, including discriminative networks, retrieval, and generation methods. Figure 2 illustrates the comprehensive framework of our approach. Our discriminative networks, denoted as ECCW, are employed for the classification and discrimination of rumor-related information. These networks incorporate Semantic Encoding to encode the raw text initially. Subsequently, the text undergoes processing through two subsidiary networks. 1) Domain Expert Discrimination: This is utilized for semantic decomposition tasks. By employing gating mechanisms, it directs semantics to domain-specific experts, thus enhancing accuracy and saving computational resources. 2) Citizen Perceptual: This is employed to simulate the diversity in individuals’ comprehension of information, akin to introducing semantic noise. These semantics then traverse through a fully connected graph network to mimic interpersonal communication. The information captured by the graph network is concatenated with the original information to simulate the process of individuals reconsidering their viewpoints after discussions with others in real-life scenarios. Finally, the classification results from the two subsidiary networks are integrated using a voting mechanism named Collective Wisdom Decision to achieve classification effectiveness. The Retrieval module encompasses the construction of a vector database. This is essential for achieving precise and real-time debunking effects while minimizing the influence of large language model hallucinations. It is impractical to rely on fine-tuning large language models for debunking explanations due to the considerable time investment involved. However, maintaining and updating a debunking database in real-time is a viable solution. When users pose queries, relevant knowledge can be swiftly retrieved from the vector database through keyword matching, enabling rapid verification of information authenticity. Generation methods involve amalgamating classification results with retrieved knowledge using prompt techniques. This ensures not only the discernment of rumor veracity but also the provision of comprehensive explanations, thereby achieving a comprehensive debunking effect throughout the entire process.

### 3.1. Semantic Encoding

We use a pre-training model to generate text embeddings, which can better capture semantic information in textual data and provide more powerful feature representation for subsequent classification tasks. LLM has a deeper understanding of linguistics, resulting in a higher expressive capability in text representation. Here, we set the sequential text as $T_{i}$, and the final sentence vector is $S_{i}$. The embedding of the text is represented by the following formula:

$$ (1) $S_{i}=Transformer(T_{i}),$ $$

$\boldsymbol{S}_{i}\in\mathbb{R}^{d}$ is the representation of the textual information, where $d$ is the embedding size. In particular, we use Chinese llama (Cui et al., ), which, based on the original vocabulary of LLaMA, adds 20,000 Chinese tokens to help prevent Chinese text from being segmented into too many fragments, thereby improving encoding efficiency. We load a pre-trained Chinese LLaMA tokenizer. Each text is processed, and the tokenizer is used to convert the text into a format that the model can handle. Subsequently, we get the final embedding vector through the pre-trained model. This embedding vector can be used for subsequent machine learning tasks, such as classification, clustering, or other NLP-related applications.

### 3.2. Domain Expert Discrimination

Figure: Figure 3. Expert Discrimination. The input will be routed through a router to allocate domain experts, and finally, the selected expert information will be summarized.
Refer to caption: extracted/2403.20204v1/img/expert.png

Due to the typically cross-domain nature of rumors, such as the convergence of financial and social science domains, it is imperative to enlist specialized domain experts for evaluating the professional credibility of pertinent domain information. To emulate this process, we introduce a hybrid expert network designed to channel semantic information into different domains, each overseen by respective experts (see Figure 3). Specifically, a Router network is responsible for analyzing the domain category to which a given message belongs and submitting it for evaluation to domain-specific experts. Each expert, having gained a distinct understanding within their respective domains, is then subject to semantic assessments by an expert ensemble network responsible for consolidating these evaluations. The network defined as follows:

$$ (2) $\mathcal{G}_{i,E}=Router(S_{i}),$ $$

where $S_{i}$ is the $i$th text vector that has been encoded semantically. $\boldsymbol{\mathcal{G}_{i,E}}\in\mathbb{R}^{E}$ is computed through a Router network, where each non-negative entry in $\mathcal{G}_{i,E}$ corresponds to an expert. This vector directs the feature to a select few experts $E$, with the non-zero entries indicating the extent to which an expert contributes to the final network output.The specific computation is shown in the following equation:

$$ (3) $\\ Router(x)=Softmax(x,W),$ $$

where $\boldsymbol{W}\in\mathbb{R}^{d\times E}$ represents the weight coefficients of the routing network, where $d$ is the text dimension and $E$ is the number of experts configured.

$$ (4) $\text{FFN}_{e}(S_{i})=wo_{e}\cdot\text{ReLU}(wi_{e}\cdot S_{i}),$ $$

$$ (5) $H_{i}=\sum^{E}_{e=1}\mathcal{G}_{i,e}\cdot\text{FFN}_{e}(S_{i}).$ $$

The input ${S}_{i}$ is processed, with $wi_{e}$ and $wo_{e}$ serving as the input and output projection matrices for the feed-forward layer (an expert), where $e$ denotes the index corresponding to the expert. The hybrid expert projection decision layer output, $H_{i}$, is then calculated as the weighted average of the outputs from the chosen experts.

### 3.3. Citizen Perceptual

Figure: Figure 4. Citizen Perceptual. The input will first pass through semantic noise simulating different people’s perspectives, then gain insights from information exchange with others’ viewpoints, and finally integrate with one’s initial perspective to form a revised viewpoint.
Refer to caption: extracted/2403.20204v1/img/citizen.png

When it comes to discerning rumors, relying solely on expert opinions is insufficient. Even experts can make mistakes. Therefore, harnessing the power of the general public is necessary. We have devised a civic perception layer to simulate the diverse perceptions of individual citizens towards the given information (see Figure 4 ), as defined below:

$$ (6) $P_{i}^{j}={\phi}(S_{i}),$ $$

$$ (7) $M_{i}^{j}={\varphi}(P_{i}^{j}),$ $$

where $\phi(\cdot)$ is the citizen diversity resolver. Specifically, we configure n MLPs to map $S_{i}$, generating n citizen understanding vectors with subtle differences, and $j=1,2,...,n$. $\varphi(\cdot)$ is a quadratic encoding layer. We employ Bidirectional Long Short-Term Memory Network (BiLSTM), which can be substituted with other networks. The purpose of doing so is to simulate the diversity in understanding of the same information among different individuals by introducing information noise. Then, to enhance the robustness of the model, we aggregate these diversified understandings of the same information through an information discussion network, defined as follows:

$$ (8) $N_{i}^{j}={Att(M_{i}^{j},{A})},$ $$

where ${A}$ corresponds to the adjacency matrix for the set of distinct semantic vectors $M_{i}$, simulating different citizens. Heuristically adjustable, the adjacency matrix can be tailored according to a specified strategy. In our methodology, we establish full connectivity, thereby constituting a globally interconnected graph that simulates the network of interactions and discussions among citizens. Implementing our global graph network involves incorporating an attention mechanism $Att(\cdot)$, as outlined below:

$$ (9) ${Att(M_{i}^{j},{A})}=\text{softmax}\left({M}_{Q}{M}_{K}^{T}\right){M}_{V}.$ $$

In the softmax mechanism, the terms $Q$, $K$, and $V$ correspond to the query, key, and value matrices, respectively. Their pivotal role within this mechanism is to facilitate the computation of attention scores.
$Q$ signifies information from the current node, serving as the basis for deriving attention scores that govern interactions with other nodes. On the other hand, $K$ encapsulates details about neighboring nodes, contributing to the determination of the relevance or importance of each neighbor in relation to the current node. Meanwhile, $V$ embodies the actual information associated with each neighboring node.

The softmax operation applied to the matrix product ${M}_{Q}{M}_{K}^{T}$ essentially calculates attention scores, determining the relative importance of different neighbors based on the compatibility of their features. Subsequently, these attention scores are utilized to weigh the corresponding values ($V$), yielding an aggregated representation that accentuates more pertinent information from the neighbors within the broader context of the graph.

Subsequently, we concatenate the citizens’ initial cognitions with those refined through network processing, culminating in the ultimate decision cognition, defined as follows:

$$ (10) ${C}_{i}^{j}=\text{concat}\left({P}_{i}^{j},{N}_{i}^{j}\right).$ $$

This concatenation approach combines the initial cognition with information refined through network processing, simulating the integration of individual understanding, obtained through autonomous cognition, and the collective understanding resulting from discussions with others, thereby reducing information loss and comprehensively considering information from various sources. It helps incorporate a more comprehensive and multi-faceted range of information into the final decision-making cognition.

### 3.4. Collective Wisdom Decision

After obtaining distinct decision cognitions from both experts and citizens, it is necessary to compute their final choices. Here, we employ softmax for this purpose, defined as follows:

$$ (11) ${E}_{i}=\text{softmax}(H_{i}),$ $$

$$ (12) ${D}_{i}^{j}=\text{softmax}(C_{i}^{j}),$ $$

where $H_{i}$ and $C_{i}^{j}$ are derived from equations (8) and (10) respectively, while ${E}_{i}$ and ${D}_{i}^{j}$ represent their decision outcomes. To further refine the decision-making process, we introduce a voting mechanism that aggregates the decision outcomes from both experts and citizens. This involves setting up a voting box, where each individual’s decision, represented by ${E}_{i}$ and ${D}_{i}^{j}$ for experts and citizens respectively, contributes to the final decision through a democratic voting approach:

$$ (13) ${F_{i}}={softmax}\left(W_{e}\cdot E_{i}+W_{c}\cdot D_{i}^{j}\right),$ $$

where $W_{e}$ represents the weight parameter matrix for experts, indicating their influence on the final decision. $W_{c}$ represents the weight parameter matrix for citizens, indicating their influence on the final decision. The introduction of this voting mechanism facilitates the achievement of collective wisdom by blending the perspectives of both experts and citizens into the final decision.

### 3.5. Retrieval-Augmented Generation

Due to the timeliness of rumors, conducting real-time fine-tuning on LLMs based on new corpora would consume significant computational resources, which is impractical. Therefore, we consider adopting the RAG approach, where by updating the vector database in real-time, we can reduce the impact of hallucination while generating more accurate and persuasive debunking content, without sacrificing the generalization ability of LLMs in the domain.

Vector database. The data source for building the knowledge base is the debunking content contained in the rumor dataset used in this experiment, ensuring the reliability of the knowledge source itself. We chunk all knowledge texts and then embed them, storing them in vector form for easy retrieval queries.In detail, we set the chunk size to 100, and utilize OpenAI’s official embedding method for ease of compatibility with future GPT-4 models.

Knowledge retrieval.
The information queries raised by users, which require confirmation, will also utilize the same embedding method used in constructing the vector database.The semantic search (He et al., ) involves comparing the question embedding with the database of text chunk embeddings:

$$ (14) $S(Q,T_{i})=\frac{Q\cdot T_{i}}{\|Q\|\|T_{i}\|},$ $$

where $Q$ represents the question embedding, and $T_{i}$ signifies the embedding of the $i^{\text{th}}$ text chunk. Subsequently, the system ranks the text chunks according to their similarity scores. From this ranked list, the top N text chunks with the highest similarity scores are selected. These chunks are deemed the most relevant to the user’s query.

Content generation.
After obtaining the discriminative result of the information (the truthfulness or uncertainty of the rumor), as well as the relevant knowledge retrieved from the vector database, we proceed to the final Retrieval-Augmented Generation through Prompt Engineering:

$$ (15) $Prompt=\psi(F,K,Q),$ $$

where $F$ denotes the discriminative outcome of ECCW regarding the rumor, $K$ signifies the retrieved relevant knowledge, and $Q$ represents the original information of the queried rumor. $\psi(\cdot)$ represents specific prompting techniques, such as CoT (Chain-of-Thought) and others.

After undergoing thorough prompt engineering, utilizing the final fused content as input for Large Language Models (LLMs) allows for obtaining debunking information, thereby effectively combating the spread of rumors containing false information.

## 4. Experiments

### 4.1. Dataset

The dataset used in this study is CHEF (Hu et al., ), the first Chinese Evidence-based Fact-checking dataset consisting of 10,000 real-world claims. The dataset spans across multiple domains, ranging from politics to public health, and provides annotated evidence retrieved from the internet.

Table: Table 1. Comparisons of fact-checking datasets. Source means where the evidence are collected from, such as fact-checking websites (FC). Retrieved denotes if the evidence is given or retrieved from the source. Annotated means whether the evidence is manually annotated.

### 4.2. Baselines

During the discrimination phase, we used multiple pre-trained language models directly connected to a classifier as baselines (Unless weights pretrained on Chinese datasets are unavailable, we will prioritize using weights pretrained on Chinese datasets.), and focused on the performance on rumor data sets in six different fields, using accuracy, precision, recall and F1 scores as evaluation indicators. These discrimination baseline models are:
BERT (Devlin et al., ),
ALBERT (Lan et al., ),
BART (Lewis et al., ),
XLNet (Yang et al., ),
RoBERTa (Liu et al., ),
DeBERTa (He et al., ) and
LLaMA2 (Touvron et al., ).

In the generation phase, we utilized the GPT-4 model (Achiam et al., ), and concurrently, we compared various prompting techniques, including zero-shot (Wei et al., ), few-shot (Work, ), and CoT (Wei et al., ), both with and without RAG (Lewis et al., ).

### 4.3. Implementation Details

Our experiment runs on a T4 GPU, with our ECCW model having 768 hidden layers, a batch size of 1024, a learning rate of 0.0001, and Adam used as the optimizer. Additionally, the training and testing datasets are divided in an 8:2 ratio.In knowledge retrieval, we configure to retrieve only the most relevant chunk. The model used for generation is GPT-4, with the temperature set to 0.

### 4.4. Performance Comparison

Table: Table 2. Performance comparison of different models and domains. Domain represents the subdivision of the dataset into subset datasets for each specific field, Total denotes the entire dataset, Acc stands for Accuracy, Pre represents Precision, Rec stands for Recall, and F1 signifies the F1 score.

### 4.5. Ablation study

Table: Table 4. Performance comparison of ablation experiments.

### 4.6. Parameter Sensitivity

Figure: Figure 5. Parameter Sensitivity

Figure 5 offers a detailed insight into the sensitivity of the model’s performance concerning variations in the number of experts and citizens, focusing on Accuracy as the evaluative metric. Analyzing the data reveals nuanced patterns and trends.
Starting with the number of experts, the data demonstrates an optimal Accuracy of 89.3% when there are 10 experts. However, the Accuracy exhibits sensitivity to changes in the number of experts, with values fluctuating between 86.81% and 88.89% when there are 6 experts. This observation suggests that while a larger pool of experts generally contributes positively to precision, the choice of the specific number is critical for fine-tuning the model. Shifting focus to the number of citizens, a positive correlation emerges between the Accuracy and an increase in the number of citizens. For instance, with 10 experts, Accuracy shows an upward trajectory from 87.95% (2 citizens) to 88.68% (10 citizens). This trend underscores the valuable impact of citizen input on enhancing precision, emphasizing the significance of diverse perspectives in decision-making. Comparing different numbers of citizens for a fixed number of experts, such as 10, further highlights the importance of citizen input. Accuracy consistently improves as the number of citizens increases, showcasing the incremental value of incorporating diverse viewpoints.

## 5. Conclusion

We innovatively propose a comprehensive debunking process. Our discriminatory component demonstrates outstanding performance across rumors in multiple domains. Moreover, considering the real-time nature of rumors and the cost of fine-tuning LLM, we introduce RAG, which can address both the updating of rumor knowledge bases and eliminate the need for fine-tuning LLM while generating high-quality debunking content. Certainly, our research also has certain limitations. Currently, many rumors themselves are not problematic, but rather, the majority of manipulation occurs within accompanying images (deepfakes). Therefore, a potential direction for future exploration is in the multimodal realm, where systems can simultaneously identify inconsistencies within images and generate explanatory content accordingly.

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 3
presentation: 2
questions: Should there be a negative sign in equation 9 instead of addition sign?
soundness: 3
strengths: The paper explores an approach that transforms a pre-trained discriminative model (in domains of image segmentation, detection, classification) into generative models. The approach explains how we can use an approximated kernel to compute the distance between sampled data points and data distribution. The proposed approach shows results on different types of discriminative models for conditional as well as unconditional generation. The paper provides theoretical analysis to explain the proposed distance metric. The paper is easy to follow and shows results on in-painting and explanation tasks.
summary: The paper proposes a generative approach that uses pre-trained discriminative models (such as DETR, DINO, ResNet) for image generation. The proposed approach learns to generate samples by calculating the distance between the data distribution and sampled point using a kernel inspired by Neural tangent Kernel and shows similarity to score function used in diffusion models. The paper proposes Discrimination for Generation ( DFG) approach that could be used in conditional and unconditional generation setting. The paper also shows analysis of how the proposed approach is equivalent to approximating score function in diffusion models. The paper shows qualitative results on generative/in-painting tasks to demonstrate the efficacy of the proposed approach.
weaknesses: It would be helpful for a reader to get a better understanding of the following:

1. It would helpful for the reader to see some discussions around the time complexity of each inference call and some comparison with other baselines. It would be great to see specific runtime comparisons and complexity analysis with baselines such as SOTA diffusion models(eg. SDv2) and GANs (eg. StyleGANs)

2. Quantitative evaluation metrics (FID, IS) on standard datasets (e.g. CIFAR-10, ImageNet) with other generative baselines (diffusion models - stablediffusion v2.1 or XL, GANs - styleGANv2) could help the readers in evaluating the efficacy of the approach as the generated sample don’t look like high quality as mentioned in the paper.


3. It would helpful for the reader to see some discussions around Energy based models and even other approaches that leverage discriminative models for image generation. For example, discussion around referred energy-based [1] or discriminative generation methods [2] and how DFG approach compares or differs.

 It would be helpful for the readers if some more related generative papers are added as references:
 [1] Duvenaud, David, et al. "Your classifier is secretly an energy based model and you should treat it like one." ICLR 2020 
[2] Santurkar, Shibani, et al. "Image synthesis with a single (robust) classifier." Advances in Neural Information Processing Systems 32 (2019)

Minor typo

line 103 similarly -> similar

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confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: + See above for weaknesses.
+ What if the method uses some other feature maps than NTK?
soundness: 2
strengths: + Novel exploration for using NTK alignment in discriminative for generation.
+ Diverse experiment setting with nice visualizations.
summary: The paper proposes to generate a data sample by aligning its NTK feature with the average NTK feature of a real dataset. The authors approximate the latter with the parameters of the trained network (e.g., for classification). Therefore, given a trained model, the algorithm iteratively updates a synthetic sample s.t. its induced Jacobian (i.e., the NTK feature wrt this trained model) is similar to the model parameters, where the output dimension is collapsed via a weighted average. The authors experiment with unconditional and conditional generation, impainting, editing, and data understanding.

---
I have interacted with the authors, read their response, and decide to keep my original evaluation. In particular, the authors did address some of my concerns, but relied on large changes to the submitted manuscript. Some other concerns, e.g., if generation via discriminative nets are even possible, are only discussed with speculation and thus not fully addressed. And new issues, such as extremely unfair comparison (e.g., Fig 11) emerge during the discussion period. I recommend the authors develop the project further and it could become a strong submission at another venue.
weaknesses: + The approximation of the average real NTK feature with trained parameters is not well explained. The text points to Radhakrishnan et al., 2023, but I fail to find a justification in that paper. 
+ The DFG dynamic weighting is not well-analyzed. Practically, what's the performance without it? Theoretically, what stationary solutions could it converge to?
+ Missing discussion on obvious generation artifacts. Since the paper claims to use discriminative networks for generation (instead of, say, purely dataset understanding), it is important to discuss whether these are limitations of the method or the idea of using discriminative networks alone.
+ Unclear how the proposed method performs "Global Explanation" or "feature visualization" (section 6.3) better than compared baselines. Should we consider the generated image as one that captures important features picked up by the discriminative network? If so, why is realism claimed as an important advantage?
+ Missing qualitative generation evaluations such as FID, human evaluation, etc.
+ Missing discussion with Dataset Distillation works [1], some of which also generate samples by aligning deep features [2], aligning gradients [3], or requiring synthetic data to induce the same classifier network [4]. These works also generate interesting-looking images that can be used for dataset understanding.


[1] Dataset Distillation. Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A Efros. arXiv 2018
[2] Dataset Condensation with Distribution Matching. Bo Zhao, Hakan Bilen. WACV 2023.
[3] Dataset Condensation with Gradient Matching . Bo Zhao, Konda Reddy Mopuri, Hakan Bilen. ICLR 2021.
[4] Dataset Distillation by Matching Training Trajectories. George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, Jun-Yan Zhu. CVPR 2022.

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confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Please address my concerns above! I highly value this paper for providing new insights into the representation space between generative and discriminative models, even though the results are not as compelling as those of generative counterparts. The findings can pave the way for next advancements in the generative era.
soundness: 3
strengths: - The method is theoretically sound, offering valuable insights in relation to score-matching frameworks, which also implicitly aim to minimize the distance between generated samples and the difficult-to-approximate data manifold.
- The idea of using Neural Tangent Kernel to implicitly map features into data manifolds is an elegant and substantial contribution.
summary: The method repurposes discriminative models as generative models by utilizing the Neural Tangent Kernel (NTK) based on the gradient of pretrained model as a function to implicitly map discriminative features into a connected space of data manifold, without additional training. This demonstrates that nearly all discriminative methods can be transformed into generative models regardless of architecture, dataset, or algorithm, though the initial results may not be as performant as later ones. With NTK technique, the paper also present a new technique to visualize features by using model parameter itself.
weaknesses: - Although the method has theoretical guarantees, the output images still lack structural richness and differ significantly from natural input image.
- In DFG part at L347, the intuition of using augmentation-invariance to emphasize the importance of each class is unclear. It is confusing because what is the relationship between importance of image class and the difference of features between original inputs and its augmented versions. Justification or empirical evidence is needed.
- Does the assumption of a corresponding minimization between Functors and their inputs always hold? (Eq. 5) The authors should provide theoretical justification or empirical evidence for this assumption. Meanwhile, is there any edge case that breaks this assumption?
- The annotation in Figure 3 is unclear; I’m confused about whether the upper row of 6 figures represents a zoomed-in version of each variable  $x_t$  below or conveys a different meaning at a specific time step. A more detailed caption is needed to describe the chart. 
- What is the choice of distance function $d(.)$
- Is the method applicable to the representation of generative models? If so, it would be interesting to see if the proposed approach could transform an unconditional diffusion model into a conditional diffusion model using discriminative models, without additional training. As in section 5.2, the authors have shown that method is extended to conditional generation in Eq 13 with score term and condition term. In this case, the score term can be instantiated with score-based model.
- The applications part is very interesting but the quality is not that impressive. The outputs exhibit noticeable artifacts and unnatural frequency patterns.
- It is recommended to include quantitative metrics on image generation to enhance the quality of the paper (e.g. FID, IS, classification accuracy for class-conditional generation).

Meta-review: The paper presents a generative approach that utilizes pre-trained discriminative models, including DETR, DINO, and ResNet, for image generation. The proposed method generates samples by computing the distance between the data distribution and sampled points using a kernel inspired by the Neural Tangent Kernel (NTK). The authors demonstrate the effectiveness of their approach through various tasks, including unconditional and conditional generation, inpainting, editing, and data analysis

The AC acknowledges the novelty of exploring NTK alignment for generative tasks in the context of discriminative models. However, the paper falls short in several critical areas. It lacks sufficient evaluation, including comparisons with other generative models on standard datasets using established evaluation metrics. Furthermore, while there has been substantial prior work on utilizing discriminative models for generation, the paper fails to adequately discuss these works or establish meaningful connections to the existing literature.

The AC believes the paper requires significant improvements before it is suitable for publication and recommends rejection in its current form.

Decision: Reject