No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models

Authors: angéline  pouget
lucas  beyer
xiao  wang
andreas peter steiner
google  deepmind
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-11 22:13:14

Abstract: We study cultural and socioeconomic diversity in contrastive vision-language models (VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to attention several important findings. First, the common filtering of training data to English image-text pairs disadvantages communities of lower socioeconomic status and negatively impacts cultural understanding. Notably, this performance gap is not captured by - and even at odds with - the currently popular evaluation metrics derived from the Western-centric ImageNet and COCO datasets. Second, pretraining with global, unfiltered data before fine-tuning on English content can improve cultural understanding without sacrificing performance on said popular benchmarks. Third, we introduce the task of geo-localization as a novel evaluation metric to assess cultural diversity in VLMs. Our work underscores the value of using diverse data to create more inclusive multimodal systems and lays the groundwork for developing VLMs that better represent global perspectives.

Main paper: 
 introduction: 
 Contrastive vision-language models (VLMs) have emerged as a powerful and versatile method to bridge the gap between visual and textual information in deep learning systems. They utilize a dual-encoder architecture to map both images and texts into a shared latent space. Representations in this latent space are learned leveraging large datasets of noisy image-text pairs from the web. Work including CLIP [46] , ALIGN [31] and SigLIP [75] validates this approach at scale with impressive zero-shot transfer results across a wide range of downstream tasks.However, due to the growing range of applications for contrastive VLMs, it is imperative to evaluate them not only with respect to standard performance metrics, such as their classification accuracy on ImageNet-ILSRCV2012 [17] or image-text retrieval performance on COCO [38] , both of which are Western-oriented [53, 16] , but also in terms of "cultural diversity. " We illustrate what we mean in Figure 1 (a) and (b). When performing zero-shot classification on images from the Google Landmarks Dataset (GLDv2) [66] , SigLIP models trained on only English-language image-text pairs (henceforth (c) 0-shot accuracy improvement [%] on the Dollar Street dataset when switching from English-only (en) to global data with English translation (globe-tl).Figure 1 : Models trained on English image-text pairs exhibit a lack of diversity when evaluated on images from other regions, sometimes confusing landmarks with similar ones located in the West.denoted en) tend to misclassify international landmarks as similar-looking landmarks located in English-speaking countries. Note that this is the currently prevalent pretraining approach in the literature. In contrast, SigLIP models trained on the full, global data (henceforth denoted globe) identify the correct landmarks.A similar observation can also be made in Figure 1 (c), where we compare the zero-shot classification accuracy of a model trained on en data to that of a model trained on globe data with its text translated to English using the Google Translate API (henceforth denoted globe-tl). As can be seen, the en model seems to be biased towards data from Western regions. Switching to globe-tl data lowers performance for images from North America and Europe but significantly improves performance for other regions such as South America and Africa that are traditionally underrepresented in AI.It is worth clarifying that cultural diversity in our context is different from "fairness" and the mitigation of societal stereotypes. While recent work has shown that CLIP models perpetuate and amplify social biases and stereotypes present in the training data [27, 7, 2, 43] , our emphasis is different. We focus on improving the ability for a VLM to recognize and accurately interpret visual and textual data from a wide range of geographical, socioeconomic and cultural contexts, such as physical surroundings, traditions, customs and everyday goods. Evaluating cultural diversity is critical because it ensures that VLMs perform well across diverse environments and that they recognize and respect the varied perspectives that exist worldwide.In this work, we present a comprehensive study of cultural and socioeconomic diversity, focusing on the impact of training data source, composition and processing (including translation), using the recently introduced SigLIP models [75] as a case study. We cover a wide range of benchmark datasets and evaluation metrics. Amongst our findings, we show that while SigLIP models trained on English-only image-text pairs (en) achieve state of the art results on popular benchmarks (ImageNet, COCO), this filtering disproportionately hurts model performance for low-income households and regions, and negatively impacts cultural diversity. Crucially, these en models achieve demonstrably lower performance on cultural diversity benchmarks even after fine-tuning on more diverse and global data. Conversely, models pretrained on the full, global data (globe, globe-tl) followed by brief English-only fine-tuning can match and even outperform the English-only baselines on the popular Western-oriented benchmarks, while also performing demonstrably better in cultural diversity benchmarks. Hence, pretraining on global data yields better foundation models.In addition, we introduce the task of geo-localization-based on datasets such as XM3600 [58] , Dollar Street [49] , GeoDE [47] and GLDv2 [66] -as a novel evaluation metric for cultural diversity. We show that, unlike, for example, XM3600 retrieval that evaluates multilinguality, geo-localization is strongly dependent on the global composition of the dataset used during pretraining.In line with Alabdulmohsin et al. [2] , we focus on contrastive learning for several reasons. These models have a wide range of applications, e.g. zero-shot classification and cross-modal retrieval, and are being increasingly adopted in critical domains like healthcare [76, 52] , and as a backbone for other models [73, 72, 41] . We study SigLIP [75] , the currently best-performing and a widely used CLIP-style model. SigLIP and CLIP operate on the same principle of aligning representations/embeddings for texts and images and the difference is only in the choice of the loss function. 
preliminaries: 
 SigLIP Overview. Given a mini-batch B = {(I 1 , T 1 ), (I 2 , T 2 ), . . . } of image-text pairs, an image encoder f (•) and a text encoder g(•), SigLIP aims to align the image embeddings x i = f (I i )/ f (I i ) 2 in the given batch with their corresponding text embeddingsy i = g(T i )/ g(T i ) 2 .The sigmoid-based loss L(B) processes every image-text pair independently with a positive label z ii = 1 for the matching pairs (I i , T i ) and a negative label z ij = -1 for all other pairs (I i , T i =j ):EQUATIONwhere t > 0 and b are learnable temperature and bias parameters.We follow Zhai et al. [75] in most aspects: we use a Vision Transformer (ViT) [18] for images and a Transformer [60] for text, both of size Base (B), with an embedding dimension of 768. Images are resized to 256 × 256 resolution and we use 16 × 16 patch size. Text input is tokenized using a model trained on mC4 [69] , a Common Crawl-based dataset covering 101 languages, with a vocabulary size of 250 k. We keep a maximum of 64 tokens. We use the modified Adafactor optimizer [54, 74] for all our experiments with an initial learning rate of 10 -3 , reverse square root decay, weight decay of 10 -4 and 50 k warmup and cooldown steps. Training batch size is 16 k. Models are developed in the Big Vision codebase [6] using Tensor Processing Units (TPUs) [32] . Each model is trained on 10 B image-text pairs (roughly 610 k steps) for about 40 k TPUv2 core-hours, so they are compared on a compute-matched regime.Pretraining Data. We base our analysis on a range of models trained on different subsets of the WebLI dataset, a high-volume image-text dataset collected from the public web [15] . Each example in our filtered subsets contains a caption in the original language as well as an English translation if the caption is not in English. Hence, we can distinguish between three different dataset variants:(1) globe, (2) en, and (3) globe-tl. Here, globe denotes the raw, multilingual data with minimal filtering applied (e.g., removing sensitive and personally identifiable information [15] ). We denote its subset that contains only English captions by en. This mirrors the filtering that is currently applied in several influential papers, including CLIP, ALIGN and SigLIP, as well as the common way [22, 21, 67, 56, 57] of using LAION [51] and DataComp [22] . The third and last variant is globe-tl, which consists of the same images as globe but with an added pre-processing step in which any non-English text is machine-translated to English. We use this variant to differentiate between the impact of multilinguality, such as low-resource languages being severely underrepresented [8] , and cultural diversity of the trained models. Since it is plausible that VLMs may not leverage their full multilingual potential when prompted in non-English languages, as has been observed for LLMs [20] , we report results for both globe and globe-tl in all experiments and highlight any notable differences.To determine statistical significance of our results, we train 3 models each for en, globe and globe-tl with different random seeds. We perform two-sample t-tests and report the 95% confidence intervals.Evaluation Data. To evaluate cultural diversity, we use five datasets: Dollar Street [49] , GeoDE [47] , GLDv2 [66] , XM3600 [58] and MaRVL [39] . These datasets satisfy the criteria of being of sufficiently high quality and collected with geographical diversity in mind, see Figure 2 .In addition, they can readily be used for evaluating contrastive VLMs without a decoder, by supporting either zero-or few-shot classification or cross-modal retrieval.The Dollar Street (DS) [49] dataset encompasses 38 479 images depicting 289 household items commonly found in everyday settings across 63 countries. Each image is tagged with object descriptors and demographic data such as region, country, and monthly income. Following Rojas et al. [49] , we map 96 topics to ImageNet classes, resulting in a subset of 21 536 images. This subset is split into training and testing sets (17 228 and 4 307 images, respectively). Notably, object tags (including the fuzzy matching to ImageNet labels) are not mutually exclusive. We use this dataset for zero-shot object classification and geo-localization. DS has been released under the CC BY-SA 4.0 license.We also evaluate our models on a geographically diverse subset of the Google Landmarks Dataset v2 (GLDv2) [66] , featuring 1 542 images representing 884 landmarks from 84 countries [35] . These for each of the evaluation datasets, only approximate in MaRVL [39] based on the 5 languages collected in the dataset. Dollar Street [49] , GeoDE [47] , GLDv2 [66] and XM3600 [20] are geographically diverse. MaRVL is included because it focuses on underrepresented regions, such as Asia and East Africa. By comparison, ImageNet examples are mostly from a few Western countries (see for instance [53] ). COCO has a nearly identical distribution to ImageNet [16] .images serve as the public and private test datasets for the 2021 Google Landmark Retrieval Challenge, providing a benchmark for evaluating landmark recognition algorithms [34] . We use it for zero-shot landmark classification using the English names of all 884 landmarks as class labels. Finally, Crossmodal-3600 (XM3600) [58] is a multilingual evaluation dataset that comprises 3 600 images accompanied by 261 375 human-generated reference captions spanning 36 languages. The dataset is sourced from the Open Images Dataset [36] , with 100 images per language. Quality assurance measures, including a post-annotation verification process, attest to the overall high quality of the captions. We report both image-caption retrieval and geo-localization results. In the few-shot geo-localization evaluation, since XM3600 does not have train and test splits, we randomly shuffle the data and use the first 1 800 images for training and the second 1 800 images for testing. The annotations are licensed under the CC BY 4.0 license.Before digging into the detailed results, we summarize our key findings:1. The currently predominant paradigm of directly or indirectly filtering the training data to English image-text pairs negatively impacts cultural diversity and disproportionately hurts communities of lower socioeconomic status, exacerbating existing disparities. Its impact is demonstrably captured by zero-shot classification accuracy on Dollar Street, GLDv2, GeoDE, and MaRVL (Section 3.1, Table 1 , Figure 3 ). This has been known for vision datasets, such as ImageNet and OpenImages [53] , but is relatively less explored in image-text pretraining data scraped from the Web. 2. The image features learned by models trained on such filtered data are less culturally diverse.We demonstrate and quantify this by introducing the few-shot geo-localization task. A linear probe on the image encoder shows en trained image encoders to be significantly less "world-knowledgeable" than globe or globe-tl trained image encoders (Section 3.2, Table 2 ). 3. These performance disparities are not reflected by, and even at odds with, the currently most popular (and often sole reported) benchmarks based on ImageNet [17] and COCO [38] .In addition, we present evidence that benchmarks used to evaluate multilinguality such as XM3600 [58] , are insufficient to evaluate models' cultural diversity (Section 3.3, Table 1 ). 4. As a potential way out of this conundrum, we find that pretraining on unfiltered (global) data followed by fine-tuning on English-only data improves cultural diversity without sacrificing performance on the popular Western-centric benchmarks. This allows practitioners and researchers to strike a balance between these otherwise competing metrics. These performance improvements across benchmarks are further enhanced by translating training data to English (Section 3.4, Figure 4 , Figure 5 ).In summary, we call for a stop of pretraining on directly or indirectly English-filtered data.3 Detailed ResultsQuestion: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
no filter for improved cultural diversity: 
 To assess models' cultural diversity, we report zero-shot classification accuracy on Dollar Street, GLDv2, GeoDE and MaRVL. This evaluation includes tasks such as recognizing common household items across different countries and income brackets (Dollar Street and GeoDE), identifying significant landmarks and places of worship (GLDv2) and categorizing image concepts (MaRVL). Detailed results are provided in Table 1 . Pretraining on globally diverse data yields substantial enhancements across all zero-shot classification metrics related to cultural diversity. However, these improvements stand in contrast to the popular benchmarks of ImageNet zero-shot accuracy and COCO retrieval scores. Given their prominence, it is not surprising that filtering training data to English image-text pairs, directly or indirectly, has quickly established itself as the preferred choice [46, 31, 75, 22, 21, 67, 56, 57] . When considering the cultural and geographical diversity of the resulting models however, a very different picture presents itself with models trained on globe-tl and globe outperforming those trained on en across all four benchmarks by a significant margin.Indeed, a more fine-grained analysis of zero-shot classification on Dollar Street confirms that filtering to English-only training data disproportionately hurts low-income and non-Western communities; The performance difference between en and globe-tl is larger for lower-income households. Also, the performance disparity between the lowest and highest income groups is 32.5% in en (from 29.9% in $0-200 income group to 62.4% in $1998+ income group), but this gap is reduced (improved) to 27.4% in globe-tl. RIGHT: MaRVL Concepts classification accuracy disaggregated by each of the five languages/regions: Pretraining on globe-tl improves performance for Indonesian, Turkish and Mandarin Chinese and yields a similar performance to en for Tamil and Swahili.see Figure 1 (right) and Figure 3 (left). In MaRVL, performance for all regions (except TA) tend to benefit from using globally diverse training data, as shown in Figure 3 (right). The difference in classification accuracy for different income groups and geographic regions is a worrying signal of the inherent biases of filtering to English-only data. Removing the English language filter unsurprisingly leads to a drop in performance for images from Western countries, as exemplified by ImageNet and COCO benchmarks, but it significantly improves performance for the rest of the world.The disparities between models' cultural diversity become even more pronounced when considering the image encoder's few-shot geo-localization performance, which we introduce in this work. This task involves learning to predict the geographical origin of an image, be it at the country or regional level, with only a limited number of training samples per location. We do this using a linear classification probe on the image representation, employing squared loss and L2 regularization -a problem that admits a closed-form solution. Our training dataset comprises a constrained number of images per location, with results reported for sample sizes of 5, 10, or 25 instances. Should the available examples for a given location in the training set fall below this threshold, we utilize all accessible samples. Our results (Table 2 ) suggest that few-shot geo-localization holds promise as a novel metric to assess cultural diversity in VLMs.Independent of the prediction target being more fine-grained (country) or more coarse-grained (region), both globe and globe-tl have a significant edge over en, as shown in Table 2 . These results suggest that models that are not trained on sufficiently diverse and global data fail to learn features that capture country-or region-specific information. Another point that is noteworthy, especially when comparing Table 1 to Table 2 , is that the difference in performance between the globe and Table 2 : Global data improves few-shot geo-localization performance significantly. Performance differences are statistically significant for all reported results at 95% confidence level (*). globe-tl models is notably smaller in the few-shot setting. This is not surprising since the standard zero-shot classification prompt templates and object or landmark names are all in English and, hence, this more closely resembles the globe-tl training data version and does not leverage the multilingual capabilities of the globe model. In the few-shot setting, by contrast, we only use the image encoder's representations and hence the impact of the text tower on the evaluation results is reduced.As shown in Table 1 , models trained on English-only data (en) perform best on Western-oriented benchmarks. New benchmarks such as XM3600 have recently been introduced to evaluate multilinguality in VLMs. To recall, XM3600 contains 100 images each from 36 different linguistic regions, captioned by native speakers in all 36 languages. At first, it might seem that performing image-text retrieval based on the English captions or the English translation of the native captions for all 3 600 images could serve as a viable signal for cultural diversity.However, when comparing our models' performance on these two tasks, we do not find any statistically significant differences between the three models. Unsurprisingly, the globe model performs best when performing retrieval on non-English captions since the other two models have only seen English texts during training. However, when evaluating all models on the English-language captions or the English translations of captions in other languages, there are no statistically significant differences between the three model variants. We hypothesize that this is because XM3600 is derived from the Open Images dataset, which contains primarily Western images [53] or, images quite similar to what is already available in English domains. Closer inspection of the XM3600 images confirms that most images from non-Western countries are likely taken by tourists. For example, among the 100 images from the Arab world, 12% are images of cars. These images do not adequately reflect cultural differences. Moreover, since the original caption language is often English, similar images are likely to have been included in the en training data, explaining why there are no statistically significant differences between the three models. When comparing retrieval scores between English captions and English translations of captions in other languages across all three models, we observe a significant difference. Upon further investigation, we found that non-English captions tend to provide more detailed descriptions of the target image, leading to higher retrieval accuracy. This discrepancy might not be associated with cultural diversity or multilinguality but rather reflects variance in annotators.Based on these findings, we argue that datasets originally created for evaluating multilinguality, such as XM3600, might not be sufficient for evaluating cultural diversity in multimodal systems.Fine-tuning. As shown in Table 1 , improving diversity generally results in a loss of performance on standard benchmarks. In general, we found that the two objectives typically compete with each other: in Figure 4 , where we take two models pretrained on either en or globe-tl data and fine-tune them on the other dataset for a short duration. Clearly, improving culturally diverse metrics is accompanied by a loss in performance on Western-oriented benchmarks and vice versa. This is even more clear when looking at the correlation coefficients across metrics of over 40 models, shown in Figure 5 (b). In Figure 5 (a), this trade-off is further visualized. We first pretrain models on either en or globe-tl for a set number of steps (varying between 100k and 600k) and we then switch to the other dataset for the remainder of the training duration. Each model is trained for approximately 610k steps in total. We plot the resulting performance at the end of training for each of these switch-over points.In line with what we see in Figure 4 , we find that very little fine-tuning is sufficient to significantly impact model performance and improve performance either for standard benchmarks (green squares) or for the cultural diversity evaluations (red circles), depending on which dataset was used at the end.Hence, if we fine-tune for more than 100k steps, final model performance is mostly determined by the dataset that the model is fine-tuned on.Notably, it is possible to improve performance on cultural understanding (e.g. improving 0-shot accuracy in GLDv2 from 44% in en to over 45.5%) without impacting ImageNet 0-shot accuracy by pretraining on globe-tl and fine-tuning that model on en data. However, the opposite does not hold. English-only pretrained models achieve lower performance on culturally diverse benchmarks even after fine-tuning on global data with the tradeoff curve being clearly suboptimal to the one observed when fine-tuning culturally diverse models on en data. Therefore, pretraining on globally diverse data before fine-tuning on English-only image-text pairs allows for a nuanced trade-off between catering to cultural diversity and achieving strong performance on well-established benchmarks.Data mixing. Besides fine-tuning, we also study the impact of mixing the two versions of data during pretraining. Because en is a subset of globe-tl, mixing is equivalent to assigning more weight to English data. Figure 5 highlights that data mixing is as good as fine-tuning (if not better) in achieving a balance between Western-oriented and culturally diverse benchmarks. Moreover, it is applicable in settings where we may have more than 2 different data splits. However, data mixing entails training new models ab initio, thus incurring higher computational cost compared to finetuning. In our setting, we observe that fine-tuning for as few as 50k steps is often sufficient. By contrast, training a new model from scratch is more than 12 times as expensive. Table 3 provides a detailed comparison between data mixing and fine-tuning for similar mixing ratios.To conclude, both fine-tuning models pretrained on globe-tl as well as choosing an appropriate data mix during training can be viable approaches to navigate the trade-off between cultural diversity and optimizing performance on Western-oriented, but well-studied benchmarks, such as ImageNet.We apply quality filtering using an internal model trained on image-text pairs to calculate the imagetext similarity score, in order to assess if our empirical findings continue to hold in this context. The model was trained on global data to make sure it accurately assesses quality for global data, of which English data is a substantial fraction, and its threshold was tuned to balance quality and quantity. We filter out about 60% of the data in our experiments. Appendix B shows that our main findings continue to hold even when quality filters are applied. For instance, quality-filtered globe-tl performs better than quality-filtered en on 0-shot Dollar Street, GLDv2, and GeoDE but performs worse on Western-oriented benchmarks, such as ImageNet and COCO retrieval. In addition, the improvement in quality-filtered globe-tl over quality-filtered en is particularly significant for few-shot geo-localization tasks.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA] Guidelines:• The answer NA means that the paper does not include theoretical results.• All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced. • All assumptions should be clearly stated or referenced in the statement of any theorems.• The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. • Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced. 
related work: 
 A range of prior work has studied biases in zero-shot classifiers related to a range of sensitive attributes including gender, race and age [1, 25, 27, 12, 24] and found that CLIP models perpetuate biases present in the training data [7, 23, 2] . More recent work extends this analysis to zero-shot performance across groups of different income levels and geographic regions [43, 78] . While several of these papers highlight the central role of training data and the potentially large impact of design choices such as the data source or filtering techniques [22] or translation of English captions for cross-modal multilingual encoders [10, 11, 45] , to our knowledge, we are the first to study the impact of image-text pair filtering and text translation on cultural understanding in contrastive VLMs.Contrastive models are usually evaluated on a range of benchmark datasets including ImageNet [50] (and variations [29, 5] ), COCO [38] and Flickr30K [71] . These datasets have been shown to reflect a heavy Western bias [53, 16, 55] . Over the last few years, a range of alternative benchmarks have been proposed, including DollarStreet [49] , GeoDE [47] , Crossmodal-3600 [58] , GLDv2 [66] , GeoNet [33] , Geo-YFCC [19] , xGQA [44] , MaXM [13] , Ego4D [26] and GD-VCR [70] . While we have used some of these in our experiments, we decided against using others for the following reasons. GeoNet uses only images from North America and Asia and is hence not sufficiently diverse. GeoYFCC contains images from 62 different countries, but Europe-centric and based on images with noisy tags [47] . xGQA mainly evaluates multilinguality: the starting point for its creation is a monolingual English dataset that was then translated. MaXM is an adaptation of XM3600 (which we already use) to multilingual VQA, which contrastive VLMs do not support. Ego4D contains images from only 9 countries with a majority of the images from English-speaking countries (US, UK). GD-VCR is a geo-diverse commonsense benchmark, but is a VQA task unsuited for contrastive VLMs.The closest work to ours is [48] , which argues that progress in global data (Dollar Street and GeoDE) has been much slower than the progress on ImageNet. Unlike their work, however, we study the impact of the training data mixture, study the impact of translation, suggest a setup where both types of metrics can be improved, and propose geo-localization as an evaluation metric. We also consider a broader set of datasets in our study, such as XM3600, GLDv2, and MaRVL.Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?Answer: [Yes] Guidelines:• The answer NA means that the paper does not include experiments.• If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not. • If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable. • Depending on the contribution, reproducibility can be accomplished in various ways.For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed. • While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example (a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm. (b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully. (c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset). (d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility.In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 
limitations and future work: 
 While our work highlights the importance of incorporating cultural and socioeconomic diversity considerations into contrastive VLMs, several limitations should be acknowledged. Firstly, our experimental results are based on recently popular contrastive, encoder-only SigLIP models. While our analysis offers valuable insights, it should be extended to generative VLMs [40, 37, 77, 3, 14, 59, 61, 63, 4, 68, 65, 64, 62] . Secondly, our work primarily highlights the importance of utilizing all available data when pretraining foundation models, but there is potential for additional measures to further improve cultural diversity, such as via regularization, data balancing, or weight averaging [30] .Table 3 : A comparison between data mixing and fine-tuning using identical ratios of en vs. globe-tl examples; e.g., en 5 : 1 globe-tl for en → globe-tl means that the model was pretrained on en data for 508k steps and then fine-tuned on globe-tl data for approximately 102k steps. Thirdly, although our study consciously separates cultural diversity and multilinguality, investigating their intersection presents an intriguing subject for future research. Fourthly, acknowledging the vagueness of the notion of culture and cultural diversity, we recognize that our experiments are mainly comparing model performance across different countries, regions, or income groups. We do not offer a precise definition of cultural diversity in the context of VLMs and do not claim to cover all aspects of cultures in our analysis. Lastly, we do not address the relationship between cultural diversity and social biases that have been shown to be perpetuated by VLMs. Exploring these connections presents an opportunity to develop more inclusive AI systems.Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [No] Justification: We use the SigLIP trainer as well as a range of pre-trained SigLIP models that have been released as part of the Big Vision codebase [6] . While we are unable to provide access to the training data, all evaluation datasets are publicly available.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
conclusion: 
 This work highlights the importance of considering cultural diversity when training contrastive VLMs. We recommend that researchers and practitioners move away from training models on English-only image-text pairs. While this approach may seem beneficial when considering performance on popular benchmarks, such as ImageNet and COCO, it discards a vast amount of valuable and culturally diverse training information and disproportionately hurts communities of lower socioeconomic status. Our findings suggest that (i) pretraining on the full dataset followed by short fine-tuning on English-only data, or (ii) pretraining on a mixture of data, achieve good performance on standard benchmarks while also promoting cultural awareness. When doing so, it is important to acknowledge that there seems to be a trade-off between optimized performance on standard benchmarks and maintaining cultural diversity. Practitioners should, hence, carefully consider the intended use case and the importance of cultural understanding of the resulting model when deciding their pretraining data mixture, also taking into account unintended biases possibly manifesting in downstream models and applications. In specific scenarios, where downstream use is limited to English and multilingualism is not required, we have found that translating the training data into English is a viable option. However, the latter approach should be applied judiciously to avoid sacrificing valuable cultural context within the data.Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes] Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
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 Appendix: To assess the impact of quality filters on our findings, we train two models, en and globe-tl, on 1B image-text pairs, following the same training setup used in the paper. Table 4 shows a summary of these results. We observe that our empirical findings also hold in this setting. For instance, quality-filtered globe-tl performs better than quality-filtered en on 0-shot Dollar Street, GLDv2, and GeoDE but performs worse on Western-oriented benchmarks, such as ImageNet and COCO retrieval. In addition, the improvement in few-shot geo-localization for quality-filtered globe-tl over quality-filtered en is particularly significant. Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 


Decision: The paper presents a timely and valuable contribution to addressing cultural bias in vision-language models (VLMs), demonstrating that training on global data improves cultural understanding—without significantly sacrificing performance on standard benchmarks. All the reviewers comments have been addressed. All the scores recommend acceptance. Congrats to the authors.

Pre-training on global data and fine-tuning with English provides a practical and simple solution for balancing performance across culturally diverse datasets. The paper introduces new evaluation tasks, such as geo-localization, which strengthen its contributions.

While some concerns remain regarding the novelty of certain findings and the clarity of the structure, the reviewers generally agree that the work raises important issues on dataset biases and will benefit the broader machine learning community. The paper’s thorough evaluations and promising results support its relevance and potential impact.

Despite minor reservations about computational costs and dataset filtering, these concerns are orthogonal to the current contribution and ripe areas for future research.

Reviews: review: paper_summary: paper_summary: The paper compares performance of VLMs trained on just English data, multilingual data and the English translations of the multilingual data, on a range of benchmarks including Western-centric ones as well tasks that involve geographically diverse inputs. The paper finds that (i) training on just English data negatively impacts cultural diversity, (ii) existing multilingual evaluations may not work as well as intended due to their images still being Western-centric, (iii) it is possible to improve cultural understanding without sacrificing performance on the popular Western-centric benchmarks
strength_weakness: strength_weakness: 1. In the "Summary of Findings" section, the claims should be made more specific and grounded in evidence, e.g. 

(i) "exacerbating existing disparities" --> the authors should show how much the performance *gap* between best and worst groups increase by training on **en** versus **globe**, 

(ii) instead of saying "world knowledgeable", quote specific numbers and findings.

2. In figure 1 (and other parts of the paper), the authors claim that models trained on only English data do poorly on geographically benchmarks, e.g. by confusing landmarks with similar ones in Western countries. However, this result is expected given that the **en** data is a subset of **globe**, and the landmarks test may be in-distribution for models trained on **globe** and out-of-distribution for those trained on **en**. I think the paper's claim could be made stronger by better controlling for these confounding factors (e.g. by removing test-set knowledge contamination).

3. Besides, I find the dataset construction used in the experiments unrealistic: the authors state that "globe denotes the raw, multilingual data with minimal filtering applied" - many VLMs are trained on highly filtered multilingual datasets (e.g. LAION)
questions: questions: 1. Since **en** is a subset of **globe** (and thus assumably containing fewer training samples), are all models just trained until convergence? How are the different training set sizes controlled for in the experiments?

2. In Conclusion, the authors claim that "there seems to be a trade-off between optimized performance on standard benchmarks and maintaining cultural diversity" - I thought the paper already shows in Section 3.4 that this trade-off could be avoided?
limitations: limitations: The authors have sufficiently address the limitations.
paper_summary: paper_summary: For the field of training contrastively learned-based VLM, this paper firstly displays that training from English data would lead to worse cultural diversity in zero-classification evaluation. By discarding the influence of languages in evaluation, this paper proposed a geo-localization task, which could observe a trained model underperforms globe and globe-tl data. Besides, the paper also explores the possible strategies for achieving a better trade-off between standard performance and cultural diversity. According to the observations, the authors found that pre-train on the globe and fine-tuning in English is the optimal strategy with less cost.
strength_weakness: strength_weakness: - The writing is not clear enough. The structure and relation between different parts of this paper is not clear.
- The necessity of the proposed geo-localization task remains unclear in this paper. We don’t know the necessity of geo-localization compared with prior metrics that could reflect the cultural diversity of constrastive VLM.
questions: questions: - Could you please restate the definition of the concept of Socioeconomic Diversity for me? Additionally, how can it be measured? What level of diversity do we expect the model to achieve?
limitations: limitations: Yes
paper_summary: paper_summary: The paper shows that vision-language models trained solely on English filtered data displays a bias towards western-centric benchmarks. They then present a simple solution for training models that perform well on globally diverse datasets while not sacrificing performance on gold standard datasets such as ImageNet. They accomplish this by training on the unfiltered dataset then fine-tuning on the relevant subset for the downstream task.
strength_weakness: strength_weakness: Overall the weaknesses are minor.

- One potential confounding factor is CLIP-filtering which most text-image models use to improve accuracy. It’s unclear how this approach would interact with this and other filtering techniques. For example, it’s known that CLIP models [1] tend to filter out non-english captions. 

- One other potential weakness is that the technique could be computationally expensive since CLIP needs to be retrained on a potentially large English subset.
questions: questions: Minor Comments: 
- It would be useful to add the size of WebLI for comparison to LAION since most CLIP-like models have been trained on LAION. 
- I think the commas in the paper have been messed up for the numbers. 



I think this statement in the contributions is a bit strong and overclaiming. The logical leap from zero-shot classification on Dollar Street to harming low socioeconomic groups is a bit far:
"filtering the training data to English image–text pairs negatively impacts cultural diversity and disproportionately hurts communities of lower socioeconomic status, exacerbating existing disparities. Its impact is demonstrably captured by zero-shot classification accuracy on Dollar Street."
limitations: limitations: Limitations are adequately addressed.
paper_summary: paper_summary: This paper discusses the impact of the common practice of filtering for English-only data on training vision-language models, showing that this negatively impacts performance on tasks covering diverse cultural regions and backgrounds. The work proposes foregoing this filtering step to improve this representation, as well as potentially using a short English-only fine-tuning stage to achieve a more acceptable balance between standard task performance and diversity.
strength_weakness: strength_weakness: The contribution of the paper seems limited, as it is unsurprising that training on global data improves performance on benchmarks measuring global data understanding. The findings are not contextualized with prior works on the effect of training CLIP-style models on large, weakly-curated datasets or for multilingual understanding [1-3]. Results are only shown for one particular type of dual-encoder VLM (SigLIP). There is also no consideration of the effect of limited resources for training, which may make training on a larger less-curated dataset less feasible and use more computational resources, which may disproportionately impact global research communities, calling into question the paper’s categorical call to stop filtering such datasets for English-language data (L161).


[1] Carlsson et al. Cross-lingual and multilingual CLIP. LREC 2022

[2] Chen et al. mclip: Multilingual clip via cross-lingual transfer. ACL 2023

[3] Cherti et al. Reproducible scaling laws for contrastive language-image learning. CVPR 2023
questions: questions: Why is only SigLIP tested? Even restricted to CLIP-style models, it seems like the same methodology could be applied to any vision-language dual-encoder model, and to initialization schemes other than the random initialization used (L577) which presumably could have a significant effect on global representation.

Can you elaborate on how filtering hurts performance for “low-income households” (L50)? The datasets used (L98 on) are explicitly chosen to cover diverse geographical regions, which includes developing countries, but it is not clear whether socioeconomic status has a clear effect on results when controlling for geographic region and/or language. This also relates to the paper title which explicitly mentions socioeconomic diversity, though the paper seems to demonstrate the effect of geographic diversity specifically.
limitations: limitations: Overall the presentation of limitations is thoughtful and fairly exhaustive. There lacks a discussion of resource and compute limitations, which affect the ability to train on larger, less-filtered datasets.

Comments: title: Response
Thank you
Response to rebuttal
Official Comment by Reviewer mxHk
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comment: Thank you for the additional clarification! I support the acceptance of the paper as it helps bring attention to the biases and implications of training on English-only data. But I would keep my original rating of "Technically solid, moderate-to-high impact paper, with no major concerns" rating given my overall assessment of the contributions.
Dear reviewer, 

Thank you for engaging with us. Please see our response below: 

**Q1.** We apologize for not responding clearly to this point. We do agree that the claim of "exacerbating existing disparities" is supported empirically by looking into the performance difference between best and worst groups. That was actually what we meant to refer to in our earlier response. We will make this more explicit in the revised version of the paper. In dollar street, the performance gap between low-income groups (0-200) and high-income groups (>1998) is 32.5% using English-only data. The gap shrinks to 27.4% using *globe-tl*. We will add the full table below, of accuracy disaggregated by income leel, to the supplementary materials. 

| Data | | 0-200 (worst) | | 201-684 | | 685-1997 | | >1998 (best) | | Performance Gap |
| ---- | - |------------ | - | --------- | - | ---------- | - | ------ | - | --------------- |
| en | | 29.9 | | 44.4 | | 55.5 | | 62.4 | | 32.5 |
| globe | | 32.9 | | 45.1 | | 56.3 | | 62.3 | | 29.4 |
| globe-tl | | 35.0 | | 48.1 | | 56.9 | | 62.4 | | 27.4 |

**Q2.** For quality filtering, we used an internal CLIP-like model that was previously trained on image-text pairs to calculate the image-text similarity score. The model was trained on global data to make sure it has a good assessment of quality for global data, of which English is a substantial fraction, and its threshold was tuned to balance between quality and quantity. We filter out about 60% of the data in our experiments.

We hope these answer your questions, and resolve your remaining concerns.
Thank you for the rebuttal and the new results. The authors' response still doesn't completely resolve my concerns with regards to the following points:

Q1: The "exacerbating existing disparities" claim - I suggested backing up this claim by looking into the performance *difference* between best and worst groups, from training on en versus globe, but the authors only reported the worst-group performance. As mentioned earlier, overall the claims in "Summary of Findings" should also be more appropriately phrased and corroborated.

Q2: How was the "quality score filter based on image-text similarity" implemented? Was it tuned separately for en and globe-tl? More details would be helpful.
Thanks for the response which addressed most my concerns. I would keep my rating.
Dear reviewer,

Thank you for engaging with us and for adjusting your rating. We appreciate the detailed and constructive comments, and we are happy that we have addressed your concerns. We will make sure to highlight these points in future revisions of our work.

Sincerely
Thank you for your thoughtful response. My concerns are generally addressed, particularly the point that all experiments are compute-matched. I would recommend highlighting this in the revised version and acknowledging that underrepresented communities may have limited compute resources. I am still not fully convinced that the result is unexpected (it seems natural that landmarks are more captured in global data) but the findings seem overall valuable to the community and I have adjusted my rating accordingly.
Dear reviewer,

Thank you again for the detailed and constructive comments.

As the discussion period is about to end, we would like to ensure we've addressed all of your questions and concerns. If you feel we have satisfactorily responded, please let us know. Otherwise, please let us know your remaining concerns so we can address them before the discussion period closes.

Thank you
Dear reviewer, 

Thank you for engaging with us. We appreciate the detailed and constructive comments, and we are happy that we have addressed your concerns. 

Sincerely
Dear reviewer,

Thank you again for the detailed and constructive comments.

As the discussion period is about to end, we would like to ensure we've addressed all of your questions and concerns. If you feel we have satisfactorily responded, please let us know. If you have any further questions, we are happy to address them before the discussion period closes.

Thank you
Dear reviewer,

Thank you again for the detailed and constructive comments.

As the discussion period is about to end, we would like to ensure we've addressed all of your questions and concerns. If you feel we have satisfactorily responded, please let us know. If you have any further questions, we are happy to address them before the discussion period closes.

Thank you
I thank the authors for their response. My concerns have been addressed in the rebuttal. I am maintaining my rating of weak accept. My justification is that the paper provides some good empirical insights on how to handle english and non-english data for large-scale VLM training. I think the scope and generality of the findings are relatively small which is why my score isn't higher, but I like the paper and think it could be useful for other researchers.