reviews: 
Comments: The paper presents a novel approach using a simplex diffusion model for denoising labels generated by large language models (LLMs). The method aims to address the inherent noise in LLM-generated labels by leveraging an iterative denoising process within a continuous probability simplex space. The approach is motivated by the high cost and time demands of manual labeling, coupled with the practicality of LLMs for fast, albeit noisy, label generation. The paper introduces several key assumptions, including the local consistency of embedding spaces and the ability of diffusion models to gradually refine noisy labels into accurate representations. Experiments across different noise types, including symmetric, asymmetric, and instance-dependent noise, demonstrate the model's robustness. The results suggest that the method effectively reduces noise without requiring additional labeled data, making it a potentially valuable contribution to applications reliant on large-scale automatically generated labels.
Strengths: The paper has several notable strengths across originality, quality, clarity, and significance:

Originality: The approach is innovative in applying a simplex diffusion model to iteratively denoise noisy LLM-generated labels. Using the probability simplex space to handle label noise is a creative application, enabling a gradual refinement of noisy labels, which is distinct from traditional denoising methods. This method also addresses the growing need for handling noise in labels generated by LLMs, making the approach highly relevant and timely.

Quality: The paper presents a structured approach with a combination of techniques, such as embedding-based candidate selection, to effectively manage noise. The experiment setup is comprehensive, covering multiple noise types (symmetric, asymmetric, instance-dependent), which reflects the robustness of the method. By comparing the proposed model to various baselines, the paper demonstrates a clear performance advantage in multiple scenarios, supporting the claim that the simplex diffusion model can enhance accuracy in noisy data environments.

Clarity: The overall structure of the paper is clear, with a logical flow from the problem formulation to the model design and experimental results. The motivation for the proposed method is well-presented, with a clear description of how each component of the simplex diffusion process contributes to the final denoised output.

Significance: The work is significant for applications that rely on LLM-generated labels, which often contain noise. By introducing a method that reduces noise without needing additional labeled data, the paper addresses a practical challenge, potentially improving the scalability and quality of labeling in real-world scenarios. This could be valuable across fields where large-scale data labeling is a bottleneck, such as natural language processing, computer vision, and medical imaging.
Weaknesses: While the paper is promising, several areas could benefit from further improvement:

The paper lacks in-depth theoretical discussion regarding why the simplex diffusion model is particularly well-suited to the task of LLM label denoising. For example, the rationale behind using iterative denoising in the simplex space could be strengthened by exploring why this approach may perform better than existing denoising methods in managing specific types of noise common to LLM-generated labels.

Key Assumptions Verification: The paper is based on several assumptions, such as the local consistency of embeddings and the relationship between training dynamics and sample quality. However, these assumptions are not directly validated in the experiments. For instance, the assumption that noisy labels will have larger mean and standard deviation distances in the embedding space could be tested and quantified to provide stronger empirical support.

The paper could provide a more detailed analysis of the noise characteristics specific to LLM-generated labels. The motivation would be more convincing if it explained how these labels differ from other types of noise and why standard denoising methods are insufficient. Understanding these nuances would clarify why the simplex diffusion model is a more suitable choice.

Although the experiments include different noise types, they do not explore the method’s limitations in scenarios where noise is highly complex or includes extreme cases. Additionally, there is a lack of quantitative analysis regarding the training dynamics feature assumption, which would help validate the model’s robustness and provide further insights.

The paper lacks a failure analysis that would highlight the model's limitations. Discussing scenarios where the model underperforms could provide readers with a more realistic understanding of its applicability and suggest areas for future improvement.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 3