reviews: 
Comments: Vision-Language Models (VLM) have shown remarkable performance in visual scene understanding tasks especially image classification in downstream datasets. However, for a good performance, having expert annotated cleanly labeled datasets are necessary. This paper assumes downstream datasets have noisy labels. The authors, first, examine the behavior of pre-trained VLMs under three different types of textual prompts – human crafted, using synonyms and using a Large Language Model (LLM) generated description of the classes. They found that all these prompts behave distinguishably between the clean and the noisy samples. So, the authors find which of these three types of prompts gives the maximum separation between two clusters formed by the cross-entropy losses of all the examples for each class. These class specific expert prompts provide the clean and the noisy samples. For the noisy samples, a relabeling is done using the remarkable zero-shot classification capability of the VLMs. With the clean and relabeled samples, the VLM is adapted to the downstream tasks using prompt learning.
Strengths: 1.	The analysis done by the authors using different types of prompts on their capability of distinguishing between clean and noisy labeled samples is good. It provided the insight that a good prompt can indicate by virtue of the loss values on the samples, which samples may possibly be noisy. This first label filtering helps get a strong set of candidates for relabeling.
2.	The baseline analysis of directly assigning inference label to all samples, threshold driven assignment of inference label from different types of prompts to samples having different noisy labels are also good.
3.	The ablation experiments including number of given images per class, position of the prompts, number of prompts and hyperparameter sensitivity analysis adds to the experimental strength of the paper.
Weaknesses: 1.	It seems, the authors went ahead with only synthetic label noise injected into 11 datasets. However, previous works on Learning with Noisy Labels [Karim et al. (2022), Li et al. (2020b)], have additionally showed their performance on real-world large-scale noisy datasets like Clothing1M, WebVision etc. Without comparison with existing noise robust approaches in these naturally noisy datasets, the superiority of the proposed approach can not be established.
2.	One of the crucial steps of the proposed approach is to detect the samples with noisy labels effectively before relabeling. However, there isn’t any evaluation of performance on this step. It would have been good the know the precision-recall performance of the prompt based ‘Set_Distinguishing’ step where a good precision would mean greater proportion of actual clean samples in $\mathcal{D}_{cl}$ and a good recall would mean more clean samples are identified from the noisy dataset.
3.	Unlike existing prompt tuning approaches like [Khattak et al. (2023a), Wu et al. (2023)] the proposed approach did not use the VIT-B/16 or VIT-B/32 backbone for experimentations. Having such experiments would have enabled a better comparison with other approaches, not necessarily in learning with noisy labels only.
4.	Minor typo: Line 363-364: ‘are use’ -> ‘are used’. Line 393: ‘slighter’ -> ‘less’.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3