See It from My Perspective: How Language Affects Cultural Bias in Image Understanding

## Abstract

Abstract Vision-language models (VLMs) can respond to queries about images in many languages. However, beyond language, culture affects how we see things. For example, individuals from Western cultures
focus more on the central figure in an image while individuals from Eastern cultures attend more to scene context Nisbett et al. ( 2001 ) .
In this work, we present a novel investigation that demonstrates and localizes VLMs’ Western bias in image understanding.
We evaluate large VLMs across subjective and objective visual tasks with culturally diverse images and annotations.
We find that VLMs perform better on the Western subset than the Eastern subset of each task.
Controlled experimentation tracing the source of this bias
highlights the importance of a diverse language mix in text-only pre-training for building equitable VLMs, even when inference is performed in English. Moreover, while prompting in the language of a target culture can lead to reductions in bias, it is not a substitute for building AI more representative of the world’s languages. Our code/models are available at https://github.com/amith-ananthram/see-it-from-my-perspective .

## 1 Introduction

In Ways of Seeing, the art critic John Berger writes “The way we see things is affected by what we know and what we believe." This knowledge and these beliefs are informed by culture (Goldstein, ). Research from the cognitive sciences shows that culture mediates aspects of visual perception like color grouping and attentional focus Chiao and Harada ().
While many studies have found that language models exhibit a Western worldview in knowledge (e.g. entities) and beliefs (e.g. values) Xu et al. (), how vision-language models (VLMs) see things remains understudied.
Whose perspective do VLMs model? And what determines this perspective?

Recent progress in image-conditioned NLP has been driven by fusing pre-trained vision encoders with large language models (LLMs) to build VLMs (Alayrac et al., ; Liu et al., ).
By leveraging the knowledge in the weights of their LLMs, these VLMs generalize to many image understanding tasks in a zero-shot fashion (Tsimpoukelli et al., ).
Moreover, because these LLMs are often multilingual (Blevins and Zettlemoyer, ), the resultant VLMs can respond fluently to queries about images in many languages.

However, while VLMs can generate text in different languages,
they should also reflect different cultures, accurately modeling their imagery and perspectives (see Figure 1).
Prior work has shown that CLIP-style vision encoders suffer degraded performance on non-Western images in objective tasks like object identification De Vries et al. (); Richards et al. (); Nwatu et al. () due to the distribution of images seen during multimodal fusion Shankar et al. (); Pouget et al. ().
In contrast, we extend this analysis to subjective tasks and VLMs, focusing in particular on the effects of language: its distribution in text-only pre-training and its selection when prompting.
We address two key research questions: 1)
Is state-of-the-art VLM performance better on images and labels from the West than on images and labels from the East? 2) How does language affect this bias?

We systematically evaluate VLMs across culturally diverse image understanding tasks ranging from the objective to the subjective (Figure 3):
object identification (Rojas et al., ), question answering (Goyal et al., ; Zhou et al., ), and art emotion classification (Mohamed et al., ).
In every task, we find that nearly all the VLMs we evaluate exhibit a Western bias, one that is only sometimes reduced by prompting in Chinese.

Next, to better understand the effect of language on bias in image understanding, we train comparable VLMs; specifically, we train multiple LLaVa variants (Liu et al., ) by fusing CLIP (Radford et al., ) with the $7$B-parameter instruction-tuned variants of Llama2 and Baichuan2, which were pre-trained on $2$ trillion (T) tokens of English and English/Chinese respectively (Touvron et al., ; Yang et al., ). We find that using a more balanced language mix during LLM pre-training reduces model bias in multimodal tasks, even when prompting in English. Moreover, while prompting in Chinese leads to reductions in bias, these reductions are much larger when Chinese is well represented during pre-training.
These findings are more pronounced in subjective tasks like art emotion classification, but surprisingly, they still hold for objective tasks such as object identification where we might otherwise expect the pre-training language distribution to have no effect.
Finally, while we might hope to see similar reductions by simply using a multilingual fusion corpus, we show this is no replacement for an LLM pre-trained on large quantities of Chinese text.

These results have important implications for the development and deployment of VLMs. Though models are better able to leverage culturally-relevant associations when prompted in the language spoken by a target culture, they do so much more effectively when that language was common during LLM pre-training. AI risks perpetuating existing hegemonies online,
resulting in algorithmic monoculture (Bender et al., ). In fact, concurrent work has shown that Western bias in VLM performance is being exacerbated as models scale (Richards et al., ). Truly representative and democratic multimodal AI requires investment in multilingual foundation models.

In summary, our contributions are:

- 1.
We show that state-of-the-art VLMs exhibit a Western bias on both objective and subjective visual tasks; this bias is not consistently reduced by prompting in Chinese.
- 2.
We trace this Western bias to the language mix seen by VLMs during text-only pre-training; a more balanced mix reduces VLM bias, even when prompting in English.
- 3.
While prompting in Chinese can reduce Western bias, it is much more effective when Chinese was common during pre-training.
- 4.
Both a balanced pre-training language mix and prompting in Chinese yield significant reductions in bias for subjective tasks; interestingly, objective tasks also see reductions.

Figure: Figure 2: Our approach. Step 1: We measure the Western bias of off-the-shelf ($\text{OTS}_{i}$) VLMs on culturally diverse image understanding tasks by comparing their performance on each task’s Western and Eastern splits. Step 2: We train comparable multilingual VLMs (mLLaVA). We explore three model design choices focused on language: *(A)* the language mix in the pre-training corpus of the base LLM; *(B)* the prompting language; and *(C)* the language mix in the multimodal fusion corpus. We test each mLLaVA variant, measuring the effects of *(A), (B),* and *(C)* on Western bias in image understanding.
Refer to caption: extracted/5673222/images/06_13/approach.png

## 2 Related Work

Cognitive science has shown that individuals from Western and Eastern cultures exhibit regular differences in perception when categorizing colors, grouping objects and attending to images (Bornstein, ; Goh et al., ; Nisbett et al., ).

In the language only setting, prior work has probed models for such cultural alignment, finding that they exhibit a Western bias in the world knowledge (Naous et al., ) and values (AlKhamissi et al., ) they encode. In the multimodal setting, the focus has been on characterizing Western bias in object detection and scene understanding (De Vries et al., ; Liu et al., ; Richards et al., ; Nwatu et al., ), tracing it to the distribution of images used for multimodal fusion (Shankar et al., ; Pouget et al., ). Proposed methods for reducing this bias rely on painstakingly constructing static knowledge bases (Yin et al., ) from Wikipedia (which can reflect this same Western lens, Naous et al. ()) or complicated prompting pipelines tightly coupled to a single task (Song et al., ). Moreover, many methods assume that diverse concepts from different cultures are interchangeable (for example the Chinese ehru and Western drums), potentially stripping away culturally specific nuance (Zeng et al., ; Li and Zhang, ).

In contrast, we measure Western bias in both objective and subjective tasks where we want to preserve cultural differences. Moreover, while prior work has emphasized the effect of the pre-training image distribution on image understanding, we investigate the effects of the pre-training language distribution. Language reflects the lived experience of the individuals who use it (Andreas, ). Ye et al. () find that human written and machine generated image captions exhibit language-specific regularities. We extend their work to other image understanding tasks, focusing on cultural regularities and the language modeling choices that produce them. As most text online is unimodal Villalobos et al. (), this understanding allows us to better leverage this valuable resource to build more representative VLMs.

## 3 Methods

We illustrate our approach in Figure 2.
In step $1$, we evaluate off-the-shelf ($\text{OTS}_{i}$) VLMs on culturally diverse image understanding tasks. We measure their Western bias by comparing their performance on each task’s Western and Eastern splits. In step $2$, we train comparable multilingual VLMs (mLLaVA),
measuring the effects of different language modeling choices on Western bias.

Figure: Figure 3: Examples from our culturally diverse image understanding tasks which range from the objective to the subjective: object identification, question answering and art emotion classification.
Refer to caption: extracted/5673222/images/06_13/task_overview.png

### 3.1 Tasks

We select three culturally diverse tasks to evaluate Western bias in image understanding (Figure 3): the objective tasks of object identification and question answering and the subjective task of art emotion classification.
Each task exhibits image diversity (images from diverse places) and/or label diversity (labeled by diverse annotators).

For each task $T$, we use images from North America/Western Europe and annotations by English language speakers as our Western evaluation set, $X_{\text{west}},Y_{\text{west}}$; and we use images from China and annotations by Chinese language speakers as our Eastern evaluation set,
$X_{\text{east}},Y_{\text{east}}$. Neither choice is meant to be wholly representative of the West or the East. Instead, we have selected widely recognized exemplars of both. Moreover, we assume both the place an image was taken and the language spoken by an annotator are reasonable proxies for culture, a simplification made in other work as it is otherwise hard to stratify evaluation tasks by culture (Goldstein, ; Ye et al., ).

In the Eastern setting, we focus on the culture of China for several reasons. First, as it is the highest resourced of all non-Western cultures, the bias we measure here should be a floor for the bias relative to other cultures;
as other cultures are less resourced, the problem should be worse.
Second, our goal is to identify the effect of language on any Western bias we measure. This requires controlled experimentation with LLMs pre-trained on equivalently large quantities of non-Western language.
This restricts us to Chinese Xu et al. ().

#### 3.1.1 Defining Bias

Our focus is error disparity – given an evaluation metric $M$ and a task $T$ with two disjoint sets $X_{A},Y_{A}$ and $X_{B},Y_{B}$ (where $X$ are inputs, $Y$ are their labels and $A,B$ are values of attribute $\mathcal{R}$), a model $f_{\theta}$ that is unbiased with respect to $\mathcal{R}$ should perform similarly on $X_{A}$ and $X_{B}$
(Dwork et al., ; Shah et al., ; Czarnowska et al., ).
That is, we expect:

$$ $M(f_{\theta}(X_{A}),Y_{A})=M(f_{\theta}(X_{B}),Y_{B})$ (1) $$

The quotient between these two values is the bias of $f_{\theta}$ with respect to $\mathcal{R}$.
As our concern is Western bias, $\mathcal{R}$ is culture, and our metric is:

$$ $\text{bias}_{f_{\theta}}=\frac{M(f_{\theta}(X_{\text{west}}),Y_{\text{west}})} {M(f_{\theta}(X_{\text{east}}),Y_{\text{east}})}$ (2) $$

which measures how many times as good $f_{\theta}$ is on the Western subset of $T$ as on its Eastern subset. For example, if $f_{\theta}$ were unbiased at task $T$, we would expect its accuracy on its Western subset to be similar to its accuracy on its Eastern subset and $\text{bias}_{f_{\theta}}=1$. However, if the accuracy of $f_{\theta}$ were twice as high on $T$’s Western subset, $\text{bias}_{f_{\theta}}=2$.

### 3.2 Bias Characterization

To characterize the current state of Western bias in image-conditioned language generation, we select a representative set of $n$ off-the-shelf VLMs, $\{\text{OTS}_{i}:1\leq i\leq n\}$, and evaluate each one on our selected tasks. Then, we measure the Western bias of each $\text{OTS}_{i}$ (see “Step 1" in Figure 2).

We evaluate each $\text{OTS}_{i}$ in a zero-shot, open-generation setting with no additional task specific fine-tuning. This is because most fine-tuning datasets for these tasks skew Western (they contain images from Western countries annotated by English speakers).
Fine-tuning on such a dataset would complicate our evaluation of the underlying VLM’s bias. Additionally, given the trend in NLP towards large general purpose VLMs we believe that the zero-shot setting is also the most realistic.

We evaluate each $\text{OTS}_{i}$ with English prompts and prompts in the majority language of our target culture, Chinese. This allows us to test whether VLMs model the perspective of a particular culture more effectively when queried in its language.

#### 3.2.1 Evaluating Generations: M 𝑀 M italic_M

Many of the $\text{OTS}_{i}$ VLMs we evaluate exhibit limited capacity for in-context learning. Therefore, to fairly evaluate their performance, we prompt them in an open-ended setting and use another LLM to either evaluate their generations for correctness when the label set is very large, as it is for object identification and visual question answering, or to extract the nearest valid label when the label set is small, as it is for art emotion classification. This LLM is part of our evaluation metric, $M$.

Evaluating open-ended generation with LLMs has become increasingly popular in recent years as models have improved (Zheng et al., ). Recent work has found LLM judgments to be highly correlated with human ratings, especially for the short texts compared in image understanding tasks Ging et al. (). In Section 4.2.2, we validate our choice of evaluator LLM by comparing its judgments to human ratings for a sample of our generations, confirming that the evaluator LLM does not exhibit any kind of bias (model bias, West/East bias) that would confound our results.

### 3.3 Bias Sourcing

While we can characterize Western bias in existing VLMs, such a characterization does not allow us to make causal claims regarding its source. Each of the state-of-the-art VLMs we evaluate differ from one another across many different axes – in # of parameters, their pre-training corpus characteristics and in their task-specific fine-tuning.

Thus, to disentangle the effects of language modeling choices, we train our own comparable VLMs (mLLaVA).
We follow the training recipe of LLaVa: we fuse a pre-trained image encoder to a pre-trained base LLM by training on a multimodal fusion corpus of paired images and text (Liu et al., ). To allow direct comparison, we keep the image encoder and other hyperparameters fixed and systematically explore three axes of variation (the letters below correspond to mLLaVA elements in “Step 2" of Figure 2):

- A)
the language composition during pre-training of the base LLM (for the same # of tokens)
- B)
the prompting language during inference
- C)
and, the language composition (monolingual/multilingual) during multimodal fusion (for the same images and semantic content).

By training models that only vary along these axes, we can granularly test the effects of language on Western bias in image understanding.

#### 3.3.1 Measuring Reductions in Bias

Individual models will have different strengths – one might be better than another model at both the Western and Eastern split of a particular task. This task-specific strength is orthogonal to bias reduction. Some model $A$ could be much better on the Western split of a particular task and only slightly better on the Eastern split when compared to another model $B$. Even though $A$ performs better than $B$ on the Eastern split due to its strength in that task, it is more biased than $B$. Therefore, when evaluating the effects of modeling choices, we measure the resultant reduction in bias.

## 4 Experiments

### 4.1 Tasks

For each of our tasks, we pick benchmarks to be our Western and Eastern splits. For examples, see Figure 3. For summary statistics and prompts, see Table 1 and Section 8.1 of the Appendix.

#### 4.1.1 Object Identification: Dollar Street

To measure Western bias in object identification, we use the Dollar Street corpus (Rojas et al., ). This dataset contains images of $289$ everyday objects manually labeled and uploaded by individuals from around the world (exhibiting both image diversity and label diversity). Example labels include “toothbrush", “toilet" and “tv".

We only use the images from the USA and China. English labels were machine translated into Chinese(^1^11via the Google Translate API) and corrected by a native speaker.

We evaluate against this dataset in an open generation setting. As some images are annotated with multiple labels, we score VLM generations permissively, granting a model credit if a generation matches any of its image’s correct labels.

#### 4.1.2 Question Answering: VQAv2 & VLUE

To measure Western bias in question answering, we use VQAv2 and VLUE (Goyal et al., ; Zhou et al., ). VQAv2 serves as our Western split. Its images were found by querying websites like Flickr for Western concepts from ImageNet (Lin et al., ). 80%+ of the images are from North America or Europe Kirillov et al. (). They were then annotated by English language speakers. The out-of-domain VQA test set from VLUE is our Eastern split. Its images come from MARVL, where native Chinese speakers queried online sources for specific Chinese cultural concepts (Liu et al., ). The questions in VLUE were written by English speakers in the style of VQAv2. Thus, our VQA splits exhibit high image diversity and low label diversity. Like Dollar Street, we consider this task to be objective.

We randomly subsample a Western split from VQAv2 of a size similar to our Eastern split. Each question appears twice, with different images and answers. This ensures that models must consider the image to perform well (Goyal et al., ).

As our questions and answers are in English, to allow inference in Chinese, we concatenate questions and answers (to preserve short answer meanings) and translate them with the Google Translate API. A native Chinese speaker manually evaluated $50$ of these translations, assigning them an average score of $3.82/4$, indicating high quality.

#### 4.1.3 Emotions in Artwork: ArtELingo

To measure Western bias in art emotion classification, we use the ArtELingo corpus (Achlioptas et al., ; Mohamed et al., ). ArtELingo contains emotion labels and textual rationales from English and Chinese speaking annotators for $80,000$ works from WikiArt. It covers $9$ emotions: $4$ positive (amusement, awe, contentment, excitement), $4$ negative (anger, disgust, fear, sadness) and a catch-all, “something else". Each work of art receives at least $5$ annotations for each language. While this task is subjective, we consider only the works of art with moderately high agreement in a language ($\geq 3$ annotators) to ensure cultural consensus. Additionally, we ignore all cases where the majority label was “something else".

A particularly interesting subset in ArtELingo are those works of art where English annotators identified a positive emotion and Chinese annotators identified a negative emotion (or vice versa). For this opposite valence subset, we can be sure that the disagreement in labels between English and Chinese annotators extends beyond imperfect translation of emotion labels. They were each perceiving the artwork differently.

The artwork in ArtELingo was predominantly created by Western artists so this benchmark exhibits low image diversity but high label diversity.

Due to the size of the corpus, we evaluate on only its smaller “test” split when characterizing the Western bias of our off-the-shelf models. However, when we explore the sources of bias in our mLLaVA variants, we use the full corpus.

Finally, we evaluate against this dataset in an open-generation setting. While we prompt each VLM to pick among the $8$ emotions we consider, most generations are verbose. Thus, as described in Section 3.2.1, we use our evaluator LLM to extract the closest emotion to each generation.

### 4.2 Bias Characterization

#### 4.2.1 Off-the-Shelf Models: OTS i subscript OTS 𝑖 \text{OTS}_{i} OTS start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT

We select representative large VLMs that differ along multiples axes (pre-training corpus, parameter count, etc.): the English BLIP2-OPT-7B and BLIP2-Flan-T5-XXL (Li et al., ), LLaVA-0-Llama-7B, LLaVA-1-Llama-7B, LLaVA-1.5-Llama-7B and LLaVA-1.5-Mistral-7B(^2^22[https://huggingface.co/SkunkworksAI/BakLLaVA-1](https://huggingface.co/SkunkworksAI/BakLLaVA-1)) (Liu et al., , ); the bilingual (English/Chinese) Qwen-VL-7B and Qwen-VL-7B-Chat (Bai et al., ); and the multilingual mBLIP-mT0-XL and mBLIP-Bloomz-7B (Geigle et al., ).

#### 4.2.2 Evaluating Generations: M 𝑀 M italic_M

As we generate text from both our off-the-shelf models $\text{OTS}_{i}$ and our mLLaVA variants in an open-ended setting, we use a separate LLM, Mistral-7B-Instruct-v0.2, to evaluate VLM generations for both Dollar Street and VQA (Jiang et al., ). We opt for Mistral instead of GPT-4 as it is significantly cheaper (using GPT-4 to score all of our generations would cost nearly $10,000) and perfectly reproducible (the weights are fixed and publicly available). We validate Mistral’s effectiveness by comparing its judgments to those from native English and Chinese speakers for a sample of our generations. It performs similarly to GPT-4 and exhibits no West/East bias. See Section 8.2 and Table 2 for details.

For ArtELingo, as it only has $8$ emotion labels, we use Mistral to select the emotion closest to the one described in each VLM generation rather than using it as a scorer. We use this extracted emotion prediction to calculate accuracy and F1.

### 4.3 Bias Sourcing

In the sections below, *(A)* and *(C)* refer to mLLaVA elements in “Step 2" of Figure 2.

#### 4.3.1 Base LLMs (A)

To measure the effect of a base LLM’s pre-training language mix on downstream bias in image understanding, we choose two base LLMs with different mixes but that are otherwise comparable: Llama2 and Baichuan2 (Touvron et al., ; Yang et al., ). We use the 7B parameter, instruction-tuned (“Chat") variants of both models, each of which was pre-trained on 2T tokens of text. Their most significant difference is the language composition of their pre-training corpora: Llama2 was predominantly trained on English while Baichuan2 was trained on a balanced mix of English and Chinese.

Figure: Figure 4: The performance of off-the-shelf $\text{OTS}_{i}$ VLMs on our evaluation tasks when prompted in English. The $y$-axis measures the performance on the Western split; the $x$-axis, the Eastern split. Most models exhibit a Western bias across all tasks (they fall above the $west=east$ line).
Refer to caption: extracted/5673222/images/06_13/ots_bias_en.png

Figure: Figure 5: The bias (Western performance divided by Eastern performance) of each of our $\text{OTS}_{i}$ VLMs when prompted in English ( ) and Chinese ( ). Markers that fall in the gold region indicate a Western bias; in the purple region, an Eastern bias. While Western bias reductions are seen across all tasks when prompting in Chinese, these reductions are not consistently seen in every model.
Refer to caption: extracted/5673222/images/06_13/ots_bias_lang_reduction.png

#### 4.3.2 Fusion Corpus Construction (C)

For each of our base LLMs, we train English, Chinese and bilingual (English and Chinese) variants. This requires $3$ comparable multimodal fusion corpora: English, Chinese and bilingual. For English, we use the fusion corpus that was constructed to train the original LLaVA models: a pre-training dataset of $595,375$ image-text pairs from CC3M and $157,712$ synthetically generated visual instructions for images from MS-COCO Lin et al. (); Liu et al. (). For Chinese, we rely on a publicly available machine translation(^3^33[https://huggingface.co/datasets/LinkSoul/Chinese-LLaVA-Vision-Instructions](https://huggingface.co/datasets/LinkSoul/Chinese-LLaVA-Vision-Instructions)) of this corpus which we ask native speakers to evaluate. While the language in this corpus is not naturally occurring Chinese, a random sample of $50$ translations received an average score of $3.76/4$, indicating high quality. For our bilingual corpus, we randomly sample half of each monolingual corpus, ensuring no image overlap between the two. This results in $3$ equivalently sized fusion corpora with semantically equivalent captions for the same set of images in English or Chinese.

#### 4.3.3 Training Details

We train $6$ mLLaVA variants ($2\text{ base LLMs}\times 3\text{ fusion corpora}$) using the publicly released LLaVA recipe (Liu et al., ). Additional details can be found in the Appendix, Section 8.3.

Figure: (a) indicates that the base LLM is the mostly English Llama2, the bilingual (English/Chinese) Baichuan2. More Chinese during text-only pre-training reduces Western bias on objective and subjective tasks when prompting in Chinese; it also reduces Western bias on subjective tasks when prompting in English.
Refer to caption: extracted/5673222/images/06_13/mllava_bias_llm_reduction.png

## 5 Results

### 5.1 Do state-of-the-art VLMs exhibit a Western bias in image understanding?

In Figure 4, we plot the Western ($y$-axis) and Eastern ($x$-axis) performance of our off-the-shelf VLMs $\text{OTS}_{i}$ on each of our evaluation tasks. 90% of the VLMs we evaluate exhibit a Western bias on each task – they lie above the $west=east$ line. This holds for more objective tasks like object identification and VQA and more subjective tasks like art emotion classification.

Moreover, in only $\mathbf{27/40}$ model task evaluations, Western bias is reduced when prompting in Chinese (Figure 5). $8$ models have reduced bias on Dollar Street, $8$ have reduced bias on VQA, and $5-6$ have reduced bias on ArtELingo. While some VLMs are less biased when prompted in Chinese, this is not consistently true for all.

Interestingly, a model that benefits from Chinese prompting in one task tends to see benefits in other tasks as well. This is the case for BLIP2-Flan-T5, LLaVA-0, LLaVA-1, LLaVA-1.5 (Llama / Mistral) and mBLIP-mT0. This suggests that the ability to leverage Chinese for better Eastern image understanding is a model-specific quality.

It is also instructive to consider models which perform well while exhibiting the least bias in English (i.e., with the shortest residuals from the $west=east$ line). The mBLIP models (the diamond and circle markers) are two examples (Geigle et al., ). One defining characteristic of these VLMs is that their base LLMs are the multilingual mT0 and Bloomz (Muennighoff et al., ). It is possible that these VLMs leverage more diverse associations from their multilingual pre-training corpora during multimodal inference. We investigate this further in the next section.

### 5.2 Bias Sourcing

In Figures 6(a), 6(b) and 6(c), we plot the reductions in bias we see due to each of our modeling choices.

First, we note that base LLMs endow their VLMs with strengths in different tasks. When a Llama2-based model is better than the Baichuan2-based model in one split (West or East) for a particular task, it is generally better at the other split too. For example, in English, the Llama2-based models are better at object identification while Baichuan2-based models are better at visual question answering and art emotion classification.

However, our focus is the bias of each model as measured by the ratio between its performance on a task’s Western and Eastern splits (Equation 2). For instance, a model with a bias of $1$ is unbiased while $2$ means it performs twice as well on the Western split. As our interest is in how bias changes with modeling choices, we measure the difference in bias between two comparable mLLaVA variants, calculating significance using a two-factor design Krzywinski (). Full results (with $p$ values) are in the Appendix, Table 3.

#### 5.2.1 How does the language mix of the LLM affect bias in image understanding?

We find that pre-training the base LLM on a more balanced mix of languages (that is, more Chinese as for Baichuan2) reduces Western bias in image understanding (Figure 6(a)).

We observe reductions in bias for both objective and subjective tasks when prompting in Chinese. For Dollar Street, we see reductions in Western bias from $1.12$ to $1.09$ (Western accuracy went from $1.12$ times the Eastern accuracy to $1.09$ times) and for VQA we see reductions of $0.99\rightarrow 0.92$. On the subjective ArtELingo, we see significant reductions in Western bias of $1.82\rightarrow 1.16$ on the full benchmark (indicated by “all") and $2.47\rightarrow 1.23$ on the subset where English and Chinese speakers disagree on the valence of the emotion (indicated by “opp val").

We also see that more Chinese during text-only pre-training significantly reduces bias for subjective tasks when prompting in English. On ArtELingo, we see reductions of $2.20\rightarrow 1.98$ and $3.45\rightarrow 2.71$ on “all" and “opp val" respectively.

Figure: Figure 7: We map hidden states from our mLLaVA VLMs for images in ArtELingo’s opp val subset to their nearest tokens Nostalgebraist (). Despite generating fluent Chinese, our Llama2 VLM’s hidden states map to English; in contrast, in our Baichuan2 VLM, they map to both English and Chinese. Moreover, in both Baichuan2 VLMs, mapped tokens are more similar to ArtELingo’s Chinese rationales.
Refer to caption: extracted/5673222/images/06_13/mllava_artelingo_probing.png

#### 5.2.2 How does the prompting language affect bias in image understanding?

Prompting in the language spoken by a target culture reduces Western bias on objective and subjective image understanding tasks but it is more effective if that language was common during LLM pre-training (Figure 6(b)).

With the mostly English Llama2 as our base LLM, we see reductions in Western bias on VQA of $1.06\rightarrow 1.00$ when prompting in Chinese. On the subjective ArtELingo, we see significant bias reductions of $2.20\rightarrow 1.82$ and $3.45\rightarrow 2.47$ on the “all" and “opp val" subsets respectively.

With the bilingual Baichuan2 as our base LLM, we see a significant reduction in bias of $1.04\rightarrow 0.92$ on the objective VQA. And on ArtELingo, we see significant reductions in bias of $1.98\rightarrow 1.16$ (on “all") and $2.72\rightarrow 1.23$ (on “opp val").

#### 5.2.3 How does the fusion language mix affect bias in image understanding?

Finally, our results show that training on a balanced language mix during multimodal fusion yields no consistent change in Western bias in objective image understanding tasks. However, on subjective tasks, a bilingual fusion corpus results in a stronger alignment between the prompting language and the visual perspective of speakers of that language, especially for English and the Western perspective (Figure 6(c)).

With Llama2, including Chinese during multimodal fusion increases Western bias on ArtELingo (“all") when prompting in English (from $2.19\rightarrow 2.65$) but including English decreases bias on “all" and “opp val" when prompting in Chinese ($1.82\rightarrow 1.40$, $2.47\rightarrow 2.12$).

With the bilingual Baichuan2, including Chinese during multimodal fusion increases Western bias on both subsets of ArtELingo when prompting in English ($1.98\rightarrow 3.40$, $2.71\rightarrow 5.50$) but including English leaves Western bias unchanged when prompting in Chinese.

#### 5.2.4 Why do Baichuan2 variants generate more culturally aligned Chinese?

The much larger reductions in bias we see from using Chinese to prompt Baichuan2 suggests that while Llama2 generates fluent Chinese, it is not effectively modeling the visual perspective of a Chinese language speaker. In Figure 7, we explore this mechanistically by decoding hidden states from our Llama2 and Baichuan2 mLLaVA variants for images in ArtELingo’s opposite valence subset using the logit lens Nostalgebraist (). For Llama2, hidden states only decode to English, even when trained to generate Chinese, while for Baichuan2, they decode to both English and Chinese. Moreover, the decoded hidden states from Baichuan2 are more similar to the rationales provided by Chinese language annotators for their annotations than the decoded hidden states from their Llama2 counterparts; unlike Llama2, Baichuan2 actually leverages associations that are culturally Chinese. Thus, our work provides corroborating evidence in image understanding (both intrinsic, via probing, and extrinsic, via task evaluation) for a prior study that showed that Llama2 “works" in English (Wendler et al., ). Technical details of this probing are in Section 8.4 of the Appendix.

## 6 Discussion and Conclusion

Our work clearly demonstrates that current VLMs exhibit a Western bias in image understanding, one that stems in part from the mix of languages included in their text-only pre-training. While we focus on Chinese for technical reasons (because it is resourced enough for controlled experimentation), we believe that this fact also lends greater weight to our results: it is a floor for Western bias. We should expect the bias relative to other less-resourced cultures to be even greater.

Prior work has shown that LLMs encode world (and cultural) knowledge from their pre-training corpora in their weights Kassner et al. (). Our work extends this analysis to VLMs. Including more Chinese in language pre-training results in a VLM with reduced Western bias in image understanding. On objective tasks, VLMs built on top of multilingual LLMs leverage relevant knowledge from pre-training to make sense of culturally diverse images. And on subjective tasks, these VLMs are better able to model a non-Western perspective, even when prompted in English.

Finally, while some work has extended VLMs to many languages via machine translation during multimodal fusion (Zhou et al., ; Geigle et al., ), our results suggest that there are limitations to such an approach. LLMs trained on large amounts of naturally occurring non-English language learn culture-specific associations from that text relevant to diverse image understanding. The short texts seen in multimodal corpora likely contain little cultural knowledge. And, if machine translation is employed to produce such a corpus, we should expect even less. The Western bias of these VLMs cannot be fixed in the final stages of model development – its remediation requires consideration from the start (Santy et al., ).

## 7 Acknowledgements

This research is being developed in part with funding from the Defense Advanced Research Projects Agency (DARPA) Cross-Cultural Understanding (CCU) program under Contract No HR001122C0034 and the ECOLE program under Contract No HR00112390060, the National Science Foundation and by DoD OUSD (R&E) under Cooperative Agreement PHY-2229929 (The NSF AI Institute for Artificial and Natural Intelligence) and DRL-2112635 (the NSF AI Engage Institute), the Columbia Center for Artificial Intelligence and Technology (CAIT) and ONR Grant N00014-23-1-2356. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense, the National Science Foundation or the U.S. Government.

## 8 Appendix

### 8.1 Inference Prompts

#### 8.1.1 Object Identification

- 1.
English: What object is in this image?
- 2.
Chinese: {CJK*}UTF8gbsn这张图片中是什么物体？

#### 8.1.2 Visual Question Answering

For visual question answering, questions (in English and in Chinese) were used as prompts.

#### 8.1.3 Art Emotion Classification

- 1.
English: What emotion does this work of art evoke?
- 2.
Chinese: {CJK*}UTF8gbsn这件艺术作品唤起了什么样的情感?

### 8.2 Evaluation Prompts

- 1.
Object Identification: A1: "[GENERATION]" A2: "[LABEL]" Does A2 refer to an object described in A1? Yes or no.
- 2.
Question Answering: Q1: "[QUESTION]" A1: "[GENERATION]" A2: "[LABEL]" Is A2 equivalent to A1 when answering question Q? Yes or no.
- 3.
Art Emotion Classification: "[GENERATION]" Which of the following emotions is closest to the one described in the previous text: amusement, anger, awe, contentment, disgust, excitement, fear, sadness. Answer in a single word.

### 8.3 mLLaVA Training Details

We use LORA (Hu et al., ) to train each mLLaVA variant in $2$ phases: multimodal pre-training and visual instruction tuning. This allows us to freeze our base LLMs, mitigating catastrophic forgetting of pre-trained knowledge (Kirkpatrick et al., ) and enabling training each variant on $4$ NVIDIA A100 GPUs in a single day.

### 8.4 mLLaVA Probing Details

To create Figure 7, for a particular mLLaVA variant, we embed every image in ArtELingo’s opposite valence subset, extracting hidden states from each layer of its base LLM. Then, we decode these hidden states using the logit lens (Nostalgebraist, ); we consider only the tokens with decoded probability greater than 40%. This results in a set of subword tokens for each image & layer. We map these tokens to the most frequent word on Wikipedia that contains that token(^4^44Using frequency lists from [https://github.com/IlyaSemenov/wikipedia-word-frequency](https://github.com/IlyaSemenov/wikipedia-word-frequency)), producing a list of words (in English and Chinese) for each layer. For a given layer, we count the number of English and Chinese words across all images to produce the “Lang Token Counts" in Figure 7.

Then, for each image, we use XLM-R (Conneau et al., ) to compute similarities between each of its decoded words at a particular layer and its English and Chinese language annotation rationales (included in ArtELingo). We take the mean of its similarities with English rationales and the mean of its similarities with Chinese rationales to produce per-layer, per-language similarity scores for each image. We average the per-language scores across all images for every layer, computing the ratio between the two. This value is depicted by the “Rationale Similarity" coloring in Figure 7.

**Table 1: Summary statistics for our chosen evaluation datasets. The asterisk on VQAv2 indicates that the corpus is subsampled. The $\dagger$ on ArtELingo indicates the use of the full corpus (rather than only the test set). ID: image diversity. LD: label diversity. For examples from each dataset, see Figure 3.**
| Task | Subset | West | East | ID | LD |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Benchmark | # Ex. | Benchmark | # Ex. |  |  |  |  |
| Object Ident. | $-$ | Dollar Street (us) | $1,012$ | Dollar Street (cn) | $1,003$ | $\uparrow$ | $\uparrow$ |
| VQA | $-$ | $\text{VQAv2}^{*}$ | $2,000$ | VLUE | $2,128$ | $\uparrow$ | $\downarrow$ |
| Art Emotions | all | ArtELingo | $2,066$ | ArtELingo | $4,254$ | $\downarrow$ | $\uparrow$ |
| $28,888^{\dagger}$ | $50,329^{\dagger}$ |  |  |  |  |  |  |
| opp. val. | $214$ | $214$ |  |  |  |  |  |
| $2,746^{\dagger}$ | $2,746^{\dagger}$ |  |  |  |  |  |  |

**Table 2: Comparison of two evaluator LLMs: Mistral-7B and GPT-4. We ask native English and Chinese speakers to manually judge a random sample of $160$ Dollar Street and $240$ VQA model generations (stratified by model and West/East). When graded against these human judgments, Mistral performs comparably to GPT-4 on Dollar Street and outperforms GPT-4 on VQA. Moreover, it does not exhibit a strong model or West/East bias, making it a good choice to evaluate our open-ended generations.**
| Task | Subset | Mistral Accuracy | GPT-4 Accuracy |
| --- | --- | --- | --- |
| Dollar Street | all | $0.8375$ | $0.8812$ |
| llama2 | $0.8375$ | $0.8875$ |  |
| baichuan2 | $0.8375$ | $0.8750$ |  |
| west | $0.8375$ | $0.8750$ |  |
| east | $0.8375$ | $0.8875$ |  |
| VQA | all | $0.9167$ | $0.8375$ |
| llama2 | $0.9250$ | $0.8500$ |  |
| baichuan2 | $0.9083$ | $0.8250$ |  |
| west | $0.9333$ | $0.8583$ |  |
| east | $0.9000$ | $0.8167$ |  |

**Table 3: The accuracy, Western biases and Western bias reductions of our mLLaVA variants on our image understanding tasks. $M$ indicates the base LLM, either the mostly English Llama2 (L) or the English and Chinese Baichuan2 (B); the subscript indicates use of a monolingual ($m$) or bilingual ($bi$) fusion corpus. $L$ indicates the prompting language. West and East indicate the accuracy on the Western and Eastern split of each task. Bias indicates the Western bias (West / East). $\Delta_{llm}$, $\Delta_{lang}$ and $\Delta_{fus}$ indicate the change in Western bias when changing the base LLM from Llama2 to Baichuan2, the prompting language from English to Chinese or the fusion corpus from monolingual to bilingual. $p$ indicates $p$-values calculated using a two-factor design Krzywinski (). Bolded entries are significant at the $\alpha=0.05$ level.**
|  | $M$ | $L$ | West | East | Bias | $\Delta_{llm}$ | $\Delta_{lang}$ | $\Delta_{fus}$ |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Bias Diff | $p$ | Bias Diff | $p$ | Bias Diff | $p$ |  |  |  |  |  |
| Dollar Street | $\text{L}_{m}$ | en | $0.789$ | $0.682$ | $1.156$ |  |  |  |  |  |  |
| zh | $0.651$ | $0.579$ | $1.124$ |  |  | $0.032$ | $0.234$ |  |  |  |  |
| $\text{B}_{m}$ | en | $0.749$ | $0.679$ | $1.103$ | $0.053$ | $0.192$ |  |  |  |  |  |
| zh | $0.696$ | $0.636$ | $1.094$ | $0.031$ | $0.682$ | $0.010$ | $0.718$ |  |  |  |  |
| $\text{L}_{bi}$ | en | $0.794$ | $0.702$ | $1.122$ |  |  |  |  | $0.024$ | $0.609$ |  |
| zh | $0.640$ | $0.602$ | $1.093$ |  |  |  |  | $0.061$ | $0.269$ |  |  |
| $\text{B}_{bi}$ | en | $0.723$ | $0.631$ | $1.109$ |  |  |  |  | $-0.043$ | $0.443$ |  |
| zh | $0.660$ | $0.630$ | $1.086$ |  |  |  |  | $0.046$ | $0.323$ |  |  |
| VQA | $\text{L}_{m}$ | en | $0.534$ | $0.506$ | $1.057$ |  |  |  |  |  |  |
| zh | $0.404$ | $0.406$ | $0.995$ |  |  | $0.062$ | $0.157$ |  |  |  |  |
| $\text{B}_{m}$ | en | $0.540$ | $0.519$ | $1.041$ | $0.016$ | $0.729$ |  |  |  |  |  |
| zh | $0.427$ | $0.464$ | $0.920$ | $0.075$ | $0.105$ | $\mathbf{0.121}$ | $\mathbf{0.008}$ |  |  |  |  |
| $\text{L}_{bi}$ | en | $0.494$ | $0.456$ | $1.084$ |  |  |  |  | $-0.027$ | $0.672$ |  |
| zh | $0.362$ | $0.356$ | $1.018$ |  |  |  |  | $-0.023$ | $0.696$ |  |  |
| $\text{B}_{bi}$ | en | $0.555$ | $0.533$ | $1.041$ |  |  |  |  | $0.000$ | $0.985$ |  |
| zh | $0.440$ | $0.473$ | $0.931$ |  |  |  |  | $-0.011$ | $0.836$ |  |  |
| ArtELingo (all) | $\text{L}_{m}$ | en | $0.292$ | $0.133$ | $2.195$ |  |  |  |  |  |  |
| zh | $0.233$ | $0.128$ | $1.822$ |  |  | $\mathbf{0.373}$ | $\mathbf{0.000}$ |  |  |  |  |
| $\text{B}_{m}$ | en | $0.291$ | $0.147$ | $1.981$ | $\mathbf{0.213}$ | $\mathbf{0.000}$ |  |  |  |  |  |
| zh | $0.367$ | $0.318$ | $1.156$ | $\mathbf{0.666}$ | $\mathbf{0.000}$ | $\mathbf{0.825}$ | $\mathbf{0.000}$ |  |  |  |  |
| $\text{L}_{bi}$ | en | $0.245$ | $0.092$ | $2.647$ |  |  |  |  | $-0.452$ | $0.076$ |  |
| zh | $0.240$ | $0.171$ | $1.401$ |  |  |  |  | $\mathbf{0.421}$ | $\mathbf{0.000}$ |  |  |
| $\text{B}_{bi}$ | en | $0.219$ | $0.065$ | $3.393$ |  |  |  |  | $\mathbf{-1.411}$ | $\mathbf{0.004}$ |  |
| zh | $0.267$ | $0.227$ | $1.178$ |  |  |  |  | $-0.022$ | $0.053$ |  |  |
| ArtELingo (opp. val.) | $\text{L}_{m}$ | en | $0.249$ | $0.072$ | $3.448$ |  |  |  |  |  |  |
| zh | $0.207$ | $0.084$ | $2.472$ |  |  | $\mathbf{0.976}$ | $\mathbf{0.000}$ |  |  |  |  |
| $\text{B}_{m}$ | en | $0.241$ | $0.089$ | $2.715$ | $0.733$ | $0.081$ |  |  |  |  |  |
| zh | $0.306$ | $0.249$ | $1.228$ | $\mathbf{1.244}$ | $\mathbf{0.000}$ | $\mathbf{1.487}$ | $\mathbf{0.000}$ |  |  |  |  |
| $\text{L}_{bi}$ | en | $0.220$ | $0.050$ | $4.389$ |  |  |  |  | $-0.941$ | $0.625$ |  |
| zh | $0.185$ | $0.087$ | $2.124$ |  |  |  |  | $0.349$ | $0.055$ |  |  |
| $\text{B}_{bi}$ | en | $0.221$ | $0.040$ | $5.505$ |  |  |  |  | $\mathbf{-2.790}$ | $\mathbf{0.032}$ |  |
| zh | $0.288$ | $0.242$ | $1.190$ |  |  |  |  | $0.039$ | $0.526$ |  |  |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: N/a
soundness: 3
strengths: The paper has some important strengths that I list below:

1. Very relevant topic considering that many SOTA VLMs used by the community are typically trained with English-only datasets and we don't know to what extent their pretraining can facilitate transfer to other cultures

2. Two important results provided by this paper: 1) a study of the bias ratio of open-source models out of the box; 2) a controlled study that investigates the capabilities of models after pretraining depending on their language model training.
summary: The authors present an interesting study that looks into the problem of Western bias in VLMs model and what role language plays in the disparity between model performance on western-centric datasets vs others. They study this problem by conducting controlled experiments for different visual understanding tasks (e.g., object identification, question answering, and art emotion classification). In this study they focus on the disparity in performance between English and Chinese and use a specific bias ratio to determine such error disparity. The main takeaway message from the study is that the underlying language model plays a big role and that it's important to make sure that the pretraining stage of the language model has a suitable level of language diversity to facilitate transfer to different cultures and languages.
weaknesses: Despite the strengths highlighted, the current paper falls short in certain aspects that I highlight below:

1. I find the structure of the paper a bit confusing because it uses the terms "bias sourcing" and "bias characterisation" as titles which haven't been described before. I believe that it would be more informative to use as titles the actual names of the experiments to make sure that the reader is aware of the corresponding content that will be described in that section.

2. The measure of bias that is reported in the current paper is misleading because it implicitly assumes that the reference metric is an accuracy. However, the authors do not acknowledge this in any way. Considering that novel models are purely generative, I believe it's important to clarify how this study can generalise to more general tasks that involve free-flow generation.

3. I think it's important to consider what is the effect of the model generations to the overall performance. I think the authors should state, together with the accuracy score, how many times the model actually provides a meaningful answer to the prompt rather than say something like "I cannot answer to this". I'm mentioning this mostly because when testing models trained in English with Chinese prompts, it might be the case that models might not follow the prompt at all. It would be useful to report this explicitly in your results (how many times does the model reply following the format provided?).

4. Using newer LLMs for their study would have been useful such as Llama-3 or Qwen instead of Llama-2. This is mostly because they are generally stronger LLMs compared to Llama-2

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: What are the architectures of Baichuan2 and Llama2? What was their training data mix? What are some other differences and similarities between the two models?
soundness: 3
strengths: The paper presents a very novel perspective in understanding VLMs. The experiments seem well designed and well executed. The conclusions could inspire interesting discussions. Overall I think this is a good paper.
summary: The paper aims to investigate if vision-language models are biased due to the language mix in the pretraining data or the instruction tuning data, by drawing inspiration from cognitive science research suggesting culture could affect visual perception. The authors measure western bias, or the ratio between performances over English-specific data and Chinese-specific data.
weaknesses: Considering English to represent the entire West and Chinese to represent the entire East Asia is over-generalizing. It is perfectly ok to discuss English bias (instead of Western bias), and it would only strengthen the paper. 

The authors seem a bit eager to reach identical conclusions on both subjective tests and objective tests, to the point that they did not discuss opposite trends on Dollar Street (but did show the results visually in Figure 5(b)). In Figure 5(b), Western bias on Dollar Street becomes worse when the prompt is in Chinese. This contradicts the description in Line 428: prompting in a culturally closer language reduces Western bias on objective and subjective tasks. 

It is common for the same instruction tuning data or the same prompting technique to have different performances on different tasks. VLM performance is just nuanced. [1] suggests that there are several VL skills learned by VLMs that could conflict. Hence, I do not think discussing this fact would weaken the paper. Rather, ignoring this fact would cause confusion and, in the worse case, be read as twisting data to suit a particular argument. 

[1] Anthony Tiong, et al. What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases. NAACL 2024.

Some additional details would be beneficial:

Section A.4 should contain a more detailed description of the logit lens, so that the paper becomes more self-contained. The numerical results should be shown as tables. 

The comparison between Baichuan2 and Llama2 should contain more detail. For example, what are their architectures? What was their training data mix? Such data are missing from the main text and the appendix. It is the authors' job to support the claim that these models are essentially identical except training data.  

Prompting in a culturally closer language reduces Western bias on objective and subjective image understanding tasks, especially if it was common during LLM pre-training -- what does "it" refer to?

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. I understand that using the Chinese-translated version of the LLaVA dataset allows for a potentially better comparison with English models. But, as reinforced by your results, it would be great to see how culturally-relevant data affects performance and bias. For this, you could explore using the Wukong dataset [3].

---
[3] Gu et al. Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark. NeurIPS 2022.
soundness: 3
strengths: 1. The paper proposes a systematic evaluation of language modeling towards multicultural image understanding in VLMs: the authors use two different pretrained LLMs as backbone (one English, and one English–Chinese); prompts in English and Chinese; and fusion corpora in English-only, Chinese-only and mixed.
2. The results corroborate the findings that earlier adoption of multilingual data in LLMs consistently benefits upstream VLMs.
3. A mechanistic study shows that the hidden states of Chinese tokens of a multilingual VLM initialized with an English backbone only decode to English, which the authors suggest is due to the model not effectively modeling the visual perspective of Chinese speakers.
summary: This paper investigates the effects of language modeling in multicultural image understanding. The authors focus on English and Chinese as proxies for Western and Eastern cultures, and show how Western bias (defined as the performance ratio on Western data over Eastern data) across different tasks varies as a factor of the base LLMs, language prompt and fusion corpora. Based on performance in three tasks, the authors argue for including multilingual and multicultural data early in model development, as opposed to later stages (fusion and prompts).
weaknesses: 1. The caption of Fig 4 says that “western bias reductions are seen across all tasks when prompting in Chinese.” However, from Fig 4, it seems that prompting in Chinese mostly exacerbates the performance of VLMs.
2. Prior work had explored the role of language in multilingual understanding and generation in VLMs, such as [1, 2]. It would be good to discuss these papers and compare your findings with theirs.
3. It would also be good to report overall performance, in addition to bias, for these models. For example, a model that performs at random will likely have a bias of 1.
4. Nit: Please make text in Figures much bigger.

---
[1] Qiu et al. Multilingual Multimodal Learning with Machine Translated Text. EMNLP 2022.

[2] Ramos et al. PAELLA: Parameter-Efficient Lightweight Language-Agnostic Captioning Model. NAACL 2024.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: * Could the authors elaborate on how their findings on language mix during pre-training could be extended to other non-Western languages and cultures?
* The findings in the paper are somewhat conventional and reflect existing consensus in LLMs. What do the authors think the same findings reflect about the relationship between vision(image) and language(text)?

I look forward to an active discussion with the authors during the rebuttal phase and will revise my score accordingly.
soundness: 3
strengths: * The paper discusses the cultural bias in VLMs, providing a comprehensive analysis of how language influences this bias in VLMs.
* The paper offers a comparative analysis between English and Chinese, two major languages, which provides valuable insights into bias reduction.
* The paper presents a comprehensive methodology, with controlled experiments to trace bias sources and measure the effects of different language modeling choices on bias.
summary: This paper investigates the Western bias in vision-language models (VLMs) and the role of language in cultural bias within image understanding. It evaluates VLMs on tasks with culturally diverse images and annotations, finding a Western bias that can be reduced by more diverse language representation during pre-training. The paper highlights the importance of multilingual foundation models for equitable VLMs and provides empirical evidence that a balanced language mix in pre-training significantly reduces bias.
weaknesses: * The study's focus on English and Chinese may limit the generalizability of the findings to other languages and cultures.
* While the paper provides evidence of bias reduction, the authors need to do a deeper analysis of why certain models exhibit less bias, potentially through a more detailed examination of their training data and processes.

Meta-review: This paper extends the analysis of cultural bias, which has primarily been explored in the context of large language models (LLMs), to vision-language models (VLMs). A strength of the study lies in its approach to not only analyze pretrained models but also conduct deeper investigations by training models under various configurations. The insights derived from this work could enhance our understanding of cultural bias in VLMs and help alleviate its impact in the future. Recognizing this strength, all reviewers assigned a score of 6, indicating “marginally above the acceptance threshold.” In line with their evaluations, I also suggest accepting the paper.

However, as pointed out by reviewers bfSC and pN9q, the paper tends to overgeneralize biases observed in English-speaking and Chinese-speaking cultures as representative of "Western" and "Eastern" biases, respectively. Reviewer bfSC specifically stated, “Fixing this issue requires narrowing down the claim, which is easily doable.” The authors had sufficient opportunity to address this concern and revise their manuscript during the discussion period but did not do so adequately.

While ICLR is primarily a machine learning conference, this does not justify the lack of rigor in the sociocultural framing and analysis, which is a critical aspect of this study. Given the significance of this issue, I also strongly recommend that the authors revise the current manuscript to address this concern.

Decision: Accept (Poster)