See It from My Perspective: How Language Affects Cultural Bias in Image Understanding
Authors: Amith Ananthram
Elias Stengel-Eskin
Mohit Bansal
Kathleen McKeown
Venue: ICLR 2025 Poster (2025)
Keywords: vision-language models
multilinguality
cultural bias
vqa
emotion classification
art
Primary area: alignment, fairness, safety, privacy, and societal considerations

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 East Asian cultures attend more to scene context (Nisbett 2001).  In this work, we characterize the Western bias of VLMs in image understanding and investigate the role that language plays in this disparity. We evaluate VLMs across subjective and objective visual tasks with culturally diverse images and annotations. We find that VLMs perform better on the Western split than on the East Asian split of each task.  Through controlled experimentation, we trace one source of this bias in image understanding to the lack of diversity in language model construction. While inference in a language nearer to a culture can lead to reductions in bias, we show it is much more effective when that language was well-represented during text-only pre-training. Interestingly, this yields bias reductions even when prompting in English. Our work highlights the importance of richer representation of all languages in building equitable VLMs.

Published as a conference paper at ICLR 2025
SEE IT FROM My PERSPECTIVE: HOW LANGUAGE AF- FECTS CULTURAL BIAS IN IMAGE UNDERSTANDING
Amith Ananthram', Elias Stengel-Eskin', Mohit Bansal', Kathleen McKeown CColumiaa University FNC Chapel Hill
amithhcs col umbia
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 East Asian cultures attend more to scene context (Nisbett al.) 2001). In this work, we characterize the Western bias of VLMs in image understanding and investigate the role that language plays in this disparity. We evaluate VLMS across subjective and objective visual tasks with culturally di- verse images and annotations. We find that VLMs perform better on the Western split than the East Asian split of each task. Through controlled experimenta- tion, we trace one source of this bias in image understanding to the lack of di- versity in language model construction. While inference language nearer a culture can lead to reductions in bias, we show it is much more effective when that language was well-represented during text-only pre-training. Interestingly, this yields bias reductions even when prompting in English. Our work highlights the importance of richer representation of all languages in building equitable VI.Ms. We make both our code and our models available at pattps://gitien com/
amith ananthram/ see it from my perspective
INTRODUCTION
In Ways of Seeing, the art critic John Berger writes "The way we see things is affected by what know and what we believe." This knowledge and these beliefs are informed by culture (Goldstein) 1957p. Research from the cognitive sciences shows that culture mediates aspects of visual perception like color grouping and attentional focus (Chiao & Harada 2008). While many studics have found that language models exhibit Western worldview in knowledge (e.g. entities) and beliefs (e.g. values) (Xu et al. 2024), how vision-language models (VLMs) see things remains understudied. Whose perspective do state-of-the-art VLMs model? And how does language affect this perspective?
Recent progress in image-conditioned NLP has been driven by fusing pre-trained vision encoders with large language models (LLMs) build VLMs (Awais et al. 2023). By leveraging the knowl- edge in their constituent LLMs, VLMs generalize to many image understanding tasks in a zero-shot fashion (Tsimpoukelli et al. 2021). Moreover, because their LLMs are often multilingual (Blevins) & Zettlemoyer 2022). some 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 T]. This is not guaranteed through translation alone. Prior work has shown that vision encoders sutfer degraded performance on non-Wesstee Western images in objective tasks like object identification (De Vries et al., 2019 Richards
al. [023)] Nwatu et al. 2023) due to the distribution of images seen during multimodal fusion (Shankar et al. [2017] Pouget al. 2024). In contrast, our focus is the effect of language bias in both objective and subjective image understanding. In particular, how does using a culturally closer language (during both text-only pre-training and prompting) affect this bias?
We begin by evaluating off-the-shelf VLMs in multiple languages on tasks with images and la- bels from representative Western and East Asian cultures: object identification (Rojas et al. 2022), question answering (Schwenk et al. 2022 Romero et al. (2024), and art emotion classification (Mo- hamed et al. 2022 Then, to isolate the effect of language on bias in these tasks, train our

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Published as a conference paper at ICLR 2025
objective
Westemn Users
Asian Users
label: home
LOURIURR
& 1 (This is picture of temple.)
X
X
West
38 A A (This artwork evokes contentment,
V
X
Fi
muttiingual
muttiinguel generatione
(Deseribe this picture.)
sudjectivee
Si K (This is picture of home.)
8# #100
Asia
(This artwork evokes sadness,
contentment
muttiingual mutticuitural VLM
muttiingual multicltuural generatione
zh label: sadness
$ EAME 7 4
(What emotion does this artwork evoke?)
Whose perspective do VLMs model? Despite being multilingual, state-of-the thr--rr VLMs exhibit a bias toward imagery and perspectives from Western culture. more balanced language mix during text-only pre-training produces VLMMs that are both multilingual and mudticultural.
Figure 1:
VLMs in a controlled setting. This restricts to languages with publicly available LLMs that are comparable and strong enough to adapt to multimodal tasks: English and Chinese (Xu al. 2024).
We find nearly all off-the-shelf VLMs exhibit Western bias in every task, performing better on examples from both the narrow set of English-speaking countries and a broader set spanning North America / Western Europe (i.e. "the West", (Spielvogel et al. (2012)) than on examples from China and its neighbors (i.c. East Asia, Choi (0100).. Moreover, this bias not consistently reduced by prompting in Chinese instead of English even though Chinese is culturally closer to East Asia.
To understand this, we train comparable LLaVa variants (Liu et al. 2024) by fusing CLIP (Radford et 1021 with the 7B 7B- parameter instruction-tumed variants of Llama2 and Baichuan2, which were pre-trained on 2 trillion (T) tokens of English and English/Chinese respectively (Touvron et al. representative language mix during I.I.M pre-
2021)
2023 Yang al. 2023). We find that using training reduces model bias in visual tasks in both the narrow set of Chinese-speaking countries and the broader of its East Asian neighbors, even when prompting in English. Moreover, while prompting in Chinese leads to reductions in bias, these reductions are much larger when Chinese well represented during pre-training. These findings are more pronounced in subjective tasks like art emotion classification, but surprisingly, they hold for objective tasks such as visual question answering where we might otherwise expect the pre- pre-training language mix have no effect. Finally, while we might hope to see similar reductions by simply using a smaller-scale multilingual fusion corpus, we show this no replacement for an LLM pre-trained on large quantities Chinese text.
These results have important implications for the development of VLMs. Though we might hope that models leverage culturally-relewe associations when prompted in language closer to a target cul- ture. they do so much more effectively when that language was common during LLM pre-training. risss perpetuating hegemonies online, resulting in algorithmic monoculture (Bender et al. 2021 In fact, Western bias in VLMs is being exacerbated models scale (Richards al. 2023 representative multimodal Al requires investment in multilingual foundation models.
Truly
Note:
To measure Western East Asian bias, we evaluate VLMs on examples with images and annotations from as many Western East Asian countries exist in published benchmarks (see Table []. This includes both English Chinese- Chinese-speaking countries and their geographical and cultural neighbors (Spielvogel et al. (2012), CCoii (2010).. While our controlled study of how language affects this bias limited to only English and Chinese due to lack of comparable multilingual LLMs, evaluation examples span this more representative set of countries. Thus, we refer to our objects of study as Western and East Asian bias as a convenient shorthand.
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Step 1: Bias Characterization
Step Bias Sourcing
Do off-the-shelf (OTS,) VLMs
How does language affect Western bias in VLMs?
have Western blas?
dog couch.
Wi
C: Fusion Corpus (mono vs bi)
biasoTs,
<l:: tHit 1HA..
W1
Ace on
Wn
Llamaa-Chat or
A: Base LLM
Baichuan2- Chat
CLIP-L
West E. Asia
West E. Asia
B: Prompt Language
Describe this image.
train
OTS;
OTS,,
zh:
mLLaVA A variants
Figure 2: Our approach. Step 1: We measure the Western bias of off-the-sheli (OTS,) VLMs on culturally diverse image understanding tasks by comparing their performance on each task's Western and East Asian 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 mL variant, measuring the effects (A), (B), and (C) on Western bias.
In summary, contributions
1. We show that VLMs exhibit a Western bias on both subjective and objective visual tasks.
2. We trace this Western bias to the language mix seen by VLMs during text-only pre- pre-training; a more balanced mix reduces VLM bias, even when prompting in English.
3. While prompting in culturally closer language
reduce Western bias in some VLMs, it is much more effective when that language is well represented during pre-training.
4. Both a balanced pre-traue pre-training language mix and prompting in culturally related language yield significant reductions in bias for both objective and subjective visual tasks.
5. A multilingual fusion corpus is not substitute for pre-training with large amounts of mul- tilingual text; addressing VLM bias requires intervention early in model development.
2 RELATED WORK
Cognitive science has shown that individuals from Western and East Asian cultures exhibit regu- lar differences perception when categorizing colors, grouping objects and attending to images
(Bornstein 1975; Goh et al. 2007, Nisbett et al. (001).
In the language only setting, prior work has probed models for such cultural alignment, finding that they exhibit Western bias in the world knowledge (Naous et al.) 2023) and values (AlKhamissi ctt al. 2024) they encode. In the multimodal setting, the focus has been on characterizing Western bias object detection and scene understanding (De Vries al. 2019 Liu al.) 2021 Richards jet al. 2023: Nwatu et al. 2023), tracing it to the distribution of images used for multimodal fusion (Shankar et al. 20117] 7 Pouget
2024). Proposed methods for reducing this bias rely on painstak- ingly constructing static knowledge bases (Yin et al. 2023) from Wikipedia (which can reflect this same Western lens, Naous al. (2023)) or complicated prompting pipelines tightly coupled single task (Song et al.) (2024). Moreover, many methods assume that diverse concepts from dif- ferent cultures are interchangeable (for example the Chinese erhu, string instrument, and Western
drums), potentially stripping away culturally specific nuance (Zeng et al. 2023 Li & Zhang 2023).
In contrast, we measure Western bias in both objective and subjective tasks where we want to pre- serve 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 people who use it (Andreas) 2022 Ye et al. (2023) find that human and machine captions exhibit language-speciffe regularities. We extend their work to other image understanding tasks, focusing cultural regularities and the language modeling choices that produce them. As most text online unimodal (Villalobos et al.) (002), this understanding allows to better leverage this valuable resource to build more representative VLMs.
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3 METHODS
We illustrate our approach in Figure In step 1, Bias Characterization, we evaluate off-the-shelf (OTS) VLMs culturally diverse visual tasks. We measure Western bias by comparing their per- formance on each task's Western and East Asian splits. In step 2, Bias Sourcing, we train comparable multilingual VLMs (mLLaVA), isolating the effects of language modeling choices on Western bias.
Tasks We select three tasks to evaluate Western bias in image understanding (Figure the objec- tive object identification and question answering and the subjective emotion classification. Each task exhibits image diversity (from diverse places) or label diversity (from diverse annotators).
For each task T, we use images and annotations from North America and Western Europe as our Western split, Xwest Yess and we use images and annotations from East Asia as our East Asian split, Xeast. Yeast- For each split, we include as many countries as are available across published benchmarks. Moreover, we assume that both the place an image was taken and the language spoken by an annotator are reasonable proxies for culture, simplification made in other work as it otherwise difficult to stratify evaluation tasks by culture (Goldstein 1957 Ye et al. 2023).
In the non- oon-Western setting, we focus on the culture of East Asia for several reasons. First, due to its inclusion of China, it is the highest resourced of all non-Western cultures. Thus, 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 we measure. This requires controlled experimentation with LLMs pre-trained on equivalently large quantities of non- wosteestern language. This restricts Chinese and East Asia [Xu et al., 2024).
any Western bias
Our focus is error disparity - given an evaluation metric M and a task T with two disjoint sets XA, Ya and XB, YB (where X inputs, Y are their labels and A, B values of attribute R), a model fo that is unbiased with respect to R. should perform similarly on XA and X (Dwork al. 2012 Shah al. 2019 Czarnowska al. 202T). That is, we expect:
Defining Bias
((fo(Xa). (f (XB),
M
(1)
Their ratio is the bias of so with respect to R. Here, R. is culture, and our metric for Western bias is: M (Xo Ywes)
bias
M (fe(Xess Asia), East Asia)
which measures how many times as good fo is on the Western split of T as on its East Asian split. For example, if fo were unbiased task T, its accuracy should be similar on both splits so bias,, = 1. However, if its accuracy were twice as high on 7's Western split, bias 2.
3.1 STEP 1: BIAS CHARACTERIZATION
We choose a representative set of n off-the-shelf VLMs, {OTS, 11 S n) and evaluate each one on our selected tasks. Then, we measure the Western bias of each OTS; ("Step 1" in Figure 2
We evaluate each OTS; zero-shot setting as most datasets for these tasks skew Western (i.e., images from Western countries annotated by English speakers). Fine-tuning on such dataset would complicate our evaluation of the underlying VL.'s bias. Additionally, given the broader trend toward large general purpose VLMS we believe that the zero-shot setting is also the most realistic.
We evaluate each OTS, twice, with English and Chinese prompts. This allows us to test whether VLMs model the perspective of a culture more effectively when queried in a related language.
3.2 STEP 2: BIAS SOURCING
While we can characterize Western bias in existing VLMs, this does not allow us to make causal claims regarding its source. Each of the VLMs we evaluate differ from one another across many different axes: parameters, pre-training corpora and task-specific fine-tuning.
Thus, to disentangle the effects of language modeling choices, train our comparable VLMs VA). We follow the training recipe of LLaVa: we fuse a pre-trained image encoder to pre- trained base LLM by training multimodal fusion corpus of paired images and text (Liu al.
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Object Identification
Question Answering
Art Emotion Classification
Dollar Street
A-OKVQA* CVQA
ArtElángo
United States, Canada, United Kingdom, United Stotes" Canada Europe", France, Spain, Ireland, Norway
Bnglish speaking ennotators
France, Spain, Haly, Netherlands, Sweden, Switzerland,, Desmurk
Western
fear
home
How is the cake decorated? Piping
East Asian
contentment
kitchenware
What food is this? Youtiao China, Mongolia, South Korea, Jupan
Chira,, Mongolia, South Korea, Cambodia, Thuiland, Vietnam
Chinese speaking annotators
Figure 3: Examples from culturally diverse image understanding tasks which range from the objective to the subjective: object identification, question answering and art emotion classification.
2024 To allow direct comparison, we keep everything 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 (for semantically equivalent prompts)
C) and, the language composition (monolingual/biinggaa)) during multimodal fusion (for the same images and semantic content).
Measuring Reductions in Bias Models have different strengths - one might be better than another at both the Western and East Asian split of particular task. This task-specific strength stems from the genres represented their pre-training (Tiong et al. 2024) and is orthogonal to bias reduction. Model A could be much better the Western split of task and only slightly better the East Asian split when compared to another model B. Even though A performs better than B on the East Asian 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 bias.
4 EXPERIMENTS
4.1 TASKS
For each of our tasks, we select relevant portions of standard benchmarks be our Western and East Asian splits (Table I]. For object identification and question answering, each split is broken into narrow language-rocusee subset of only English and Chinese speaking countries ("en/zh") and an expansive regional subset ("region"") of every available country from North America/Westerm Europe and East Asia. For examples, see Figure 3 Further details can be found in Section A.1
Object Identification: Dollar Street To measure Western bias in object identification, Dollar Street (Rojas et al.) 2022). contains images of 289 types of everyday objects manually labeled and uploaded by individuals from around the world (exhibiting image diversity and label diversity). As the labels include "toothbrush", "toilet" and "tv", we consider this task to be objective.
Chinese prompts and labels were hand- translated by native speakers. We evaluate VLMs in open generation setting. As some images have multiple labels, we judge generations permissively using an I.M,, granting credit for matching any correct label. Details can be found in Section A.I.I
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Table Our tasks: object identification (Obj. Id.), visual question answering (VQA) and art emotion classification (Art). ID and LD specify image and label diversity (J: low, 1: high). "Prompts", the source of English and Chinese prompts (HW: hrittwri,,en, MT: machine translated). "Eval", how each task is evaluated (LM: LLM-as-a-Judge, MC: multiple choice), "Western" and "East Asian", the countries/languages in each task's splits. For examples from each task, see Figure
Prompts en
ID LD
Eval
Subset
Western
East Asian
zh
en/th
US, Canada, UK France, Spain, Italy, Switzerland, Denmark, Netherlands, Sweden
China
Obj. Id.
Mongolia, S. Korea, Vietnam, Cambodia, Thailand
1 t
HW
HW
LM region
en/th
A-OKVQA
China
VQA
t t
HW
MT
MC
+ Spain, France Ireland, Norway English speaking
Mongolia,
region
S. Korea, Japan Chinese speaking annotators
Art
4 +
HW
HW
MC
opp val all
annotators
Question Answering: AOKVOA & CVQA To measure Western bias in question answering,
we use A-OKVQA and CVQA (Schwenk et al., 2022, Romero et al., 2024). A-OKVQA contains images from MSCOCO queried from websites like Flickr for Western concepts from ImageNet (Lin et al. 2014). 80%+ of the images are from North America or Europe (Kirillov et al.) [2023). Questions and 4 candidate answers (1 correct, 3 distractors) were written by English speakers on Mechanical Turk. CVQA is a complementary benchmark whose images and annotations are drawn from around the world. Annotators from 28 countries identified culturally relevant images and then annotated those images with questions and 4 candidate answers in both their native language and in English. Thus, the images, questions and answers in these benchmarks exhibit image diversity and label diversity. As these questions have globally correct answers, we consider VQA to be objective.
As our questions and answers are in English, to allow inference in Chinese, we translate questions and candidate answers with the Google Translate API. A native Chinese speaker evaluated 50 of these translations, assigning them an average score of 3.82 on a 4-point Likert scale, indicating high quality. We evaluate VLMs in multiple-choice setting, using another LLM to select the closest candidate answer from each generation to calculate accuracy. Details can be found in Section A.1.2
Emotions in Artwork: ArtELingo To measure Western bias in art emotion classification, we use the ArtLLingo corpus (Achlioptas et al.) 2021 Mohamed et al. 2022). ArtELingo contains emotion labels and textual rationales from English and Chinese speaking annotators for 80, 000 works from WikiArt. covers emotions: 4 positive (amusement, awe, contentment, excitement), 4 negative (anger, disgust, fear, sadness) and a catch-all, "something else". Each work of art receives 5 an- notations for each language. While this task is subjective, we consider only the works of art with moderately high agreement in language (2 3 annotators) to ensure cultural consensus. Addition- ally, we ignore all cases where the majority label was "something else". The artwork in ArtELingo was mostly created by Western artists so it exhibits low image diversity but high label diversity.
Of particular interest is art assigned a positive emotion by English annotators and a negative emotion by Chinese annotators (or vice versa). For this opposite valence subset, we know the disagreement extends beyond imperfect translation of emotion labels. Annotators perceived the art differently.
We evaluate VLMs on this task in open-generation setting and select the candidate emotion closest to each generation using another L.M. Details can be found in Section A.1.3
4.2 STEP 1: BIAS CHARACTERIZATION
Off-the-Shelf Models: OTS; We select VLMs that differ along multiples axes (pre-training cor-
pus. parameter count, )tc.): : the English BLIP2 (Flan-T5-XXL. el. (023). LL- (Llama-
7B & Mistral-7B [ (Liu et al. 2023);; the bilingual English/Chinese Qwen VL (7B & 7B-Chat) (Bai et al. 2023); and the multilingual mBLIP (Bloomz-7B) (Geigle et al. 2023).

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As generate open-ended text from our off-the-shelf models OTS, and our mLLaVA variants for Object Identification, we evaluate this particular task using an LLM- as-a-Judge, specifically Prometheus-2 -2-------- Bx (Kim al. 2024). Recent work has shown that L.M judgments are highly correlated with human ratings, especially for the short texts compared in image understanding tasks (Zheng et al., 2024 Ging al., (023). We use Prometheus-2 instead of GPT-4 as it is significantly cheaper and perfectly reproducible (the weights are fixed and published). We validate its effectiveness by comparing its judgments to those from native English and Chinese speakers for sample of our generations. We find that our judge LLM agrees with our human raters between 88 89% of the time in English and Chinese with Cohen's 's x values between 0.65 0.66 indicating substantial agreement (Cohen 1960). Additional details can be found in Section A.2.1
Evaluating Generations: M
For VQA and Art Emotion Classification, which are both multiple choice tasks, we use disstrll7B--nn ruct-vĆ ruct--vv 2 to extract the candidate answer closest to the one described in each generation rather than using an LLMM-as-a-uudge (Jiang et al., 2023). This extracted answer is used to calculate accuracy/F1. Additional details for these tasks are in Sections A.2.2 and A.2.3
4.3 STEP 2: BIAS SOURCING
Base LLMs To measure the effect of base LLM's pre-training language mix on bias in image un- derstanding, we choose two LLMs with different mixes but that are otherwise comparable: Llama2 and Baichuan2 (Touvron et al., 2023 Yang al. 2023). We use their 7B parameter, instruction- tuned ("Chat") variants, each pre- tre-trained on 2T tokens. Their most significant difference is the language mix during pre-training: Llama2 was trained on mostly English while Baichuan2 was trained on a balanced mix of English and Chinese. Additional details can be found in A.3.
Fusion Corpus Construction For each of our base LLMs, we train English, Chinese and bilingual (EnglishuChinese) variants, requiring 3 comparable multimodal fusion corpora. For English, we use the fusion corpus constructed for the original LLaVA models: 595, 375 image-text pre-training examples from CC3M and 157, 712 synthetic visual instructions for images from MS-COCO (Lin) cet al. 2014: Liu et al. 2024). For Chinese, we use publicly available machine translation of this corpus which we ask a native speaker to evaluate. They assigned a random sample of 50 translations an average score of 76 on 4-point Likert scale, indicating high quality. For our bilingual corpus, we randomly sample half of each monolingual corpus, ensuring no image overlap. This results in 3 fusion corpora with semantically equivalent captions in English Chinese for the same images.
Training Details We train mLLaVA variants (2 base LLMs x fusion corpora) using the pub- licly released LLaVA recipe (Liu et al. 2024). Additional details can be found in Section A.3
RESULTS
5.1 Do STATE-OF VLMS EXHIBIT A WESTERN BIAS IN IMAGE UNDERSTANDING?
In Figure 4 we plot the Western bias (Western performance divided by East Asian) of our off-the- shelf VLMS when prompted in English (0) and Chinese (X) (full results are in the Appendix, Table 5). 90%+ of these VLMs exhibit Western bias each task- they lie in the gold region. This holds for objective (object identification, VQA) and subjective tasks (art emotion classification).
Moreover, in only 15/30 model task evaluations, Western bias is reduced when prompting in Chinese (the Chinese X appears to the left of the English o). 3 models have reduced bias on Dollar Street, 3 have reduced bias on VQA, and 2 have reduced bias ArtELingo. While some VLMs are less biased when prompted in Chinese, this is not consistently true for all.
Models like Qwen-VL that benefit from Chinese prompting in one task tend see benefits in other tasks as well. This suggests that leveraging Chinese for better East Asian image understanding is a model-specific quality. It is also instructive to consider which models exhibit the least bias in
English (i.e., with leftmost o). mBLIP is one such example (Geigle et al.] (023). One defining
characteristic of this VLM is that its base L.LM is the multilingual Bloomz (Muennighoff et al.,

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ArtElingo
VQA Accurecy
Dollar Street Accuracy
Uppesile vakucw aroauik
Ezglich ve Chinse sproking
Engleh Chinee sposking
hiph-lan-
live 1. Th-
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X
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Relattvv (west/oar)
peconge in 20
Stroed west Stesed
Figure 4: The bias (Western performance divided by East Asian performance) of each of our OTS, VLMs when prompted in English (o) and Chinese (X). Markers that fall in the gold / purple regions indicate Western and East Asian biases respectively. While Western bias reductions are across all tasks when prompting in Chinese, they are not seen consistently (in only 15/30 cases).
2022 It possible uses more diverse associations from its multilingual pre-training corpus during multimodal inference, even in English. We investigate this further in the next section.
5.2
STEP 2: BIAS SOURCING
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-base better at object identification while Baichuan2-based models are better at art emotion elassification.
models are
However, our focus is bias as measured by the ratio between each model's scores on task's Western and East Asian splits (Equation [}. model with a bias of is unbiased while means it performs twice as well on the Western split. To study how bias changes with our three language modeling choices, we measure the difference in bias between two comparable mL.LaVA variants, calculating significance using two-factor design (Krzywinski] 2014). We plot these reductions along the r-axis Absolute scores (with p values) are in the Appendix, Table 6
in Figures 5a 5b and
5c
5.2. How DOES THE LLM LANGUAGE MIX AFFECT BIAS IN IMAGE UNDERSTANDING?
We find that pre-training the base LLM on more balanced mix of languages (that is, more Chinese as for Baichuan2) reduces Western bias in image understanding (Figure 5a
We observe significant reductions in bias for both objective and subjective tasks when prompt- ing in Chinese. For VQA, we see significant reductions in Western bias from 1.50 to 1.17 (West- ern accuracy went from 1.50 times the East Asian accuracy to 1.17 times) on the fine-grained EnggisshCCinnse--pcaknng country subset and reductions of 1.29 1.16 the coarse-grained regional subset. On the subjective ArtELingo, we see significant reductions in Western bias of 2.47 1.23 on the subset where English and Chinese speakers disagree the valence of the emotion ("opposite valence")) and 1.82 > 1.16 on the full benchmark ("indicated by all").
We also that more Chinese during text-only pre-training significantly reduces bias for subjec- tive tasks even when prompting in English. On ArtEL we see reductions of 3.45 - 2.71 71 and 2.20 1.98 on the "opposite valence" and "all" subsets respectively.
5.2.2 How DOES THE PROMPTING LANGUAGE AFFECT BIAS IN IMAGE UNDERSTANDING?
Prompting in culturally closer language reduces Western bias objective & subjective vi- sual tasks, especially the language was common during LLM pre-training (Figure 5b).
With the mostly English Llama2 our base LLM, we see minimal reductions in Western bias on VQA when prompting in Chinese. In contrast, using the bilingual Baichuan2 as our base LLM, we significant reduction in bias for VQA of 1.45 1.17 on the "en/zh" subset and reduction
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What the effect. more Chinese in language pre-training? VQA Accuracy
ArtELingo Aceuracy
Dollor Accuracy Deglis va C Chanss speaking
Upoeedle valeace artwock
Englsh ve CLineso speaking
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Relative (restt (vert)
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(pre-truited on Chitnce))
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Tekchana2 (jee-tninaai Chizase))
biseed nox.
(a) indicates that the hase LLM is the mostly English Llama2, X the bilingual (Fnglish/Chinese) Baichuan2. More Chinese during text-only pre-training reduces Western bias objective and subjective tasks when prompting Chinese; it also reduces Western bias on subjective tasks when prompting in English.
What the effect prompiin in Chinese!
ArrELingo Acearacy
VQA Accuracy
Dollor Street Accenec Eagllsaa ves Cainese speaking
oppoedte valence arwwols
English ve Chinese spisking
Bama12 freiuu
baicimsn?
freiim
resion
region
lamall mono fiston
bokiaun2
fasion
Relantoo Bis (oss)/)s))
Relativn (wnst/esst)
Reiation Bis (worr/roel)
promgee Enelich
Voxeed
pronge Chinase
hapool casl
(b) e indicates prompting in English. X in Chinese. While prompting in Chinese reduces Western bias, the reductions are larger when Chinese was well-represented during text-only pre-training.
What is the effect of using a bilingual fussion corpus??
Arngo Aceuracy
VQA Accenary
Dollar Street Arcurayy Erglitia vs Chinose speaking
oprecite vuleace artwolks
Englssh vs Chinese speaking
prompt Ioman
prange in sh
baldhant
prompe eq
bairhour?
prompe in sl
all artwoek
beoader region
bconder repion
prompe
prany, in di
X
X--0
bacchanc
prompe in on
pranyé
1.25
1.30
1.55
Relative Diss (wast/oxs)
Reiative Dias (west (vart)
Relative Bus (west/eust)
meceolingual Busion
blased
biiigial fextoo (Engit's Chineve)
biseed nost
(c) e indicates a monolingual fusion corpus, X bilingual (English and Chinese). On subjective tasks, a bilin- gual fusion corpus ties a prompt language to the visual perspective of its speakers, especially for English.
Figure 5: The change in bias (Western performance divided by East Asian performance) each our LA variants. Markers that fall in the gold region indicate a Western bias; in the purple region, an East Asian bias. Unbroken lines indicate bias reductions that are significant at the a = 0.05 level.
of 1.27 + 1.16 on the "broader region" " subset. On the subjective ArtELingo, our Llama2-based VLM sees significant bias reductions of 3.45 + 2.47 and 2.20 1.82 the "opposite valence" and "all" subsets when prompting in Chinese. However, for our Baichuan2-based VLM, reductions even larger: 2.72 1.23 and 1.98 1.16 on "opposite valence" and "all" respectively.
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 tasks. However, subjective tasks, bilingual fusion corpus results in a stronger alignment between the prompting language and the perspective of its speakers, especially for English and the Western perspective (Figure 5c).
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Ilama2
Ilama2 prompt in
baichnan2 prompt in en
baichuan2
prompt in en
prompt in zh
30
Rationale Similacity
1.5
Layer
25
14
20
12
Lokens
tolocns
tokeus
tokens
tokens
15
50000
100000
0
20000
400000
0
20000 40000 60000
10000
10000
20000
Lang Token Counts
Lang Token Counts
Lang Token Counts
Leng Token Counts
Figure 6: We map hidden states from our mLLaVA VA VLMs for images in ArtLingg'' opp val subset to their nearest tokens (Nostalgebraist, 2020j. Despite generating fluent Chinese, our Llama2 VLM hidden states map English; in our Baichuan2 VLM, they map to both English and Chinese. More- over, in both Baichuan2 VLMs, mapped tokens are more similar to ArtELingo's Chinese rationales.
With Llama2, including Chinese during multimodal fusion increases Western bias ArtELingo ("all") when prompting in English (from 2.19 - 2.65) but including English decreases bias on the "opposite valence" and "all"" subsets when prompting in Chinese (2.47 2.12, 1.82 + 1.40). With the bilingual Baichuan2, including Chinese during multimodal fusion increases Western bias on both subsets of ArtELingg Ling when prompting in English (2.71 5.50, 1.98 3.40) 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 bias we see from using Chinese prompt Baichuan2 suggests that while Llama2 generates fluent Chinese, it is not effectively modeling the visual perspective of a Chi- nese language speaker. In Figure 6, we explore this mechanistically by decoding hidden states from Llama2 and Baichuan2 VA variants for images in ArtELingo's 'ss opposite valence subset using the logit lens (Nostalgebraist, 2020). For Llama2, hidden states only decode to English, even when trained 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 prob- ing, and extrinsic, via task evaluation) for a prior study that showed that Llama2 "works" in English (Wendler al. 2024). Technical details of this probing are Section A.4 the Appendix.
DISCUSSION AND CONCLUSION
Our work shows that VLMS exhibit Western bias in image understanding that stems in part from the language mix in their text-only pre-training. While we focus on Chinese because it is resourced enough for controlled experimentation. this also lends greater weight to our results: 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 cultural knowledge from their pre-training in their weights (Kassner et al., 2021). We extend this analysis to VLMs. Including more Chinese in language pre- training results these VLMS leverage pre-training knowledge to make sense of diverse images. And on subjective tasks, they are better models of non-Western perspectives, even when prompted in English.
VLM with reduced Western bias in image understanding. On objective tasks,
Finally, while multilingual image-text retrieval (Ramos et al. 2024) and machine translation (Zhou jet al., 2021 Qiu et al., 2022. Geigle et al.. 2023) can both produce multilingual VLMs, our results suggest limitations to these methods. LLMs trained naturally occurring non-English language leam cultural associations relevant to diverse image understanding. The short texts in multimodal corpora likely contain little cultural knowledge. And if machine translation is employed, we should expect even less. The Western bias of these models cannot fixed in the final stages of VLM development its remediation requires consideration from the start (Santy et al. 2023).
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7 ETHICS STATEMENT
Our work relies on the simplifying assumption that both the country where an image was taken and the language in which it annotated are reliable indicators of fixed, underlying culture. Though this reduction is unfortunately necessary in Al research due to the lack of datasets with rich cultural annotations, it runs the risk of reinforeing stereotypes by stripping away important nuance.
Moreover, while we draw images from a variety of representative countries when constructing our Western and East Asian task subsets, our study of the role that language plays in VLM bias is limited to English and Chinese. This is due to the unavailability of comparable LLMs for other East Asian languages. While the assumption that English is closer to Western culture than Chinese (and that Chinese is closer to East Asian culture than English) helps explain the bias reductions we see in our results, we emphasize here that neither English nor Chinese perfectly reflects Western and East Asian cultures in their entirety. Both languages are widely spoken in countries around the world and both the West and East Asia are home to millions of people that speak neither.
While we are sensitive to these dimensions of our work, we still believe that studies like ours are an important first step toward addressing cultural bias in by providing insights regarding its source. It is our hope that future work will build ours and bridge these remaining gaps.
Finally, all vision-language models have the potential for harmful use including but not limited to surveillance. As our focus is on understanding the modeling decisions behind bias in existing VLMs, we have no additional concerns regarding harmful use beyond those relevant to the broader field.
REPRODUCIBILITY STATEMENT
To facilitate full reproducibility of results, have:
1. included comprehensive technical details in the Appendix
2. when relying on an LLM--a---Judgee chosen a model with publicly available, fixed weights
3. published both our code and models mentation
ltttp:://ittub. com/
see- pete-------rrooooo-oorrereeeeeee
with thorough docu-
4. made our models available the broader research community HuggingFace
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 HR001 122C0034 and the ECOLE program under Contract No HR001 12390060, the National Sci- ence Foundation and by DoD OUSD (R&E) under Cooperative Agreement PHY-2229929 (The NSF AI Institute for Artificial and Natural Intelligence) and DRI-21 1635 (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 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.
Additionally, we'd ''d like to thank Fei-Tzin Pei-Tzin Lee and our ICLR reviewers for their thoughtful feedback on earlier versions of this work.
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A APPENDIX
A.1 TASK DETAILS
A.1.1 OBJECT IDENTIFICATION
1. English: What object is in this image?
2. Chinese: #
These two prompts, in English and Chinese, were hand-written by NLP graduate students who are native English and Chinese speakers. Additionally, Dollar Street's class set of 289 labels was manually translated into Chinese by native Chinese NLP graduate student.
A.1.2 VISUAL QUESTION ANSWERING
For visual question answering, questions and candidate answers from A-OKVQA and CVQA were concatenated together and used as prompts, for example:
1. English: ball.
What is the dog trying to catch? Choose exactly one from person, frisbee, kite,
2. Chinese:
MA T
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English prompts drawn directly from both corpora. Chinese prompts produced from the English prompts through the Google Translate API. We asked native Chinese NLP graduate student to rate whether not each of random sample of 50 of these machine translations were "a good translation of the English" on 4-point Likert scale (with indicating strongly disagree, 2 indicating disagree, 3 indicating agree and and 4 indicating strongly agree). They received an average rating of 3.82 indicating high quality.
A.1.3 ART EMOTION CLASSIFICATION
1. English: What emotion does this work of evoke?
2. Chinese:
These prompts, in English and Chinese, were hand-written by NLP graduate students who native English and Chinese speakers.
A.2 TASK EVALUATION DETAILS
A.2.1 OBJECT IDENTIFICATION
We judge our English and Chinese language generations for our Object Identification task against their correct labels in text-only setting using "rometheus-2-8x" (Kim et al. (2024). We use the following rubric in Prometheus produce numerical scores for each generation.
instruction Identify the object (s) in this image.
criterion Does the model's answer describe the same object (or
kind of object) as the reference?
The model's answer describes an object that is completely unrelated to the reference answer. score2_description = The model's answer describes an object that is very different from the reference answer. score3 deserripicciininn The model's answer describes object similar to the reference answer.
scorel_description
acore4_descriptin
The model's answer describes specific instance of the reference answer.
score5 score5_ descriptioon The model's answer describes the same object as the reference answer
To improve the quality of these judgments. we apply self-consistency (Wang et al. 2022): each model generation is judged times (with a temperature 1.0 and nucleus sampling threshold of 0.9). We consider the mean score and assign model credit if it is greater than or equal to 3.0.
We validate the effectiveness of Prometheus BB as judge for object identification through a comparative human evaluation. We ask two native English speakers and two native Chinese speak- ers, all of whom are graduate students NLP, to manually judge random sample 100 English and 100 Chinese model generations respectively. Specifically, we ask them to assign generation credit "the generation describes an object listed the label". They complete these annotations without access to the source image and consider concatenation of all correct labels for each image.
the Table 2, we present numan-human and human-machine agreement numbers English and Chinese across representative samples from our Western (the United States) and East Asian (China) splits. For English generations, we find that Prometheus exceeds human-human agreement with a mean agreement of 89% and mean Cohen's 's к of 0.65 when compared to each human annotator individually. We observe similar mean agreement/ mean Cohen's 's for Chinese generations though in this case these values are lower than the human-huma setting (95% and 0.85).
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Table 2: Human-human and mean human-machine agreement (percent agreement and Cohen's x) judging 200 open-ended generations on the Object Identification task where images are sampled from the United States and China subsets of Dollar Street (Rojas al. 2022).
Prompt Language
Image Country
Human
Prometheus-22 8x7B
%
%
English
0.83
0.51
0.89
0.65
English
United States
0.82
0.36
0.91
0.65
English
China
0.84
0.61
0.86
0.63
Chinese
0.95
0.85
0.88
0.66
Chinese
United States
0.92
0.77
0.82
0.55
Chinese
China
0.98
0.94
0.92
0.80
A.2.2 VISUAL QUESTION ANSWERING
We first translate all Chinese generations into English using the Google Translate API. Then, for each question-generation pair, Mi Inral-l---sstrutt.... extract the nearest candidate answer using the following prompt:
(Jiang al. 2023 to
"Question: [QUESTION], Answer: [GENERATION in ENGLISH]" Which of the follewing answers is closest to the one the previous text?
1) [CANDIDATE\_] in ENGLISH] 2) [CANDIDATE], 2 in ENGLISH]
3) [CANDIDATE\_3 in ENGLISH] or 4) [CANDIDATEX, in ENGLISH).
Respond concisely with single number.
If a model's response contains multiple answers, choose the first that occurs in the generation. If contains no answers, a candidate is chosen at random.
A.2.3 ART EMOTION CLASSIFICATION
For each model generation English and Chinese, we use L..stral-7B-Instructee Jiang et al. 2023 extract the most similar emotion using the following prompt:
[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.
a model's response contains multiple answers, we choose the first that occurs in the generation. it contains answers, candidate is chosen at random.
A.2.4 MODEL REFUSALS
For our mLLaVA variants, measured the fraction of generations that were refusals. To identify such cases, we counted all generations that were 1) an empty string or 2) judged to be a refusal by another LLM, -nsttttttt 3, using the following prompt:
[GENERATION] Does the quoted text. contain refusal to answer? Please respond in a single word, \ \"yes\" or \"no\".
We found that less than 0.1% of our generations met either of these refusal criteria.
A.3 MLLAVA TRAINING
A.3.1 LLAMA2 VS BAICHUAN2
In training our mLLAVA variants, we strove to select base LLMs that were as close to each other as possible except along axis of inquiry, the language mix of their pre-training corpora. This led
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Published as a conference paper at ICLR 2025
us Llama2 and Baichuan2, billion parameter LLMs pre-trained on around 2T tokens and then subsequently alignment tuned. While their most significant difference the language mix of their pre-training corpora (90% English, 0.13% Chinese for Llama2 and bilingual English & Chinese for we compare their architectural features.
Baichuan2), in Table 3
Table 3: Architectural features of Llama2 and Baichuan2.
Feature
Ilama2
Baichuan2
BPE?
yes
yes
Split Digits?
yes
yes
Context Length
4K
4K
Vocabulary Size
32K
125K
Positional Embeddings
rotary
rotary
Activation
SwiGLU
SwiGLU
Layers
Attention Heads
32
32
Precision
float16
b6loat16
Architecturally, the two models very close. Their most significant difference is the vocabulary size of their tokenizers, These tokenizers were learned to accomodate each model's pre-training corpus (mostly English for Llama2 and bilingual English / Chinese for Baichuan2). While we know that the size of these pre-training corpora were similar, neither technical report provides detail on their composition beyond language mix. Moreover, both models were alignment tuned through su- pervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). While the SFT corpora for both models are similarly sized (between 50k and 100k), the Baichuan2 paper does not provide details on their preference tuning beyond saying that their reward model "has a perfor- mance consistent with that of Llama2" (Yang et al. 2023). Given what has been published about both models (and other English and Chinese LLMs), we felt that Llama2 and Baichuan2 were the most comparable open weight LLMs for investigating the role of language in image understanding.
A.3.2 CHINESE FUSION CORPUS
We use the pre-training and visual instruction tuning corpora from the original LLaVA pa- per as our English multimodal fusion corpus. For Chinese, we use a publicly available ma- chine translation of this corpus from: hhttps::/hugginngacce http://hgggggaaaa..cccaaat/tt//a
Vision- Instruction.
To validate the quality of this machine translation, we asked NLP graduate student and native Chinese speaker to rate whether or not each of random sample of 25 pre-training examples and 25 visual instruction tuning examples were "a good translation of the English" on a 4-point Likert scale (with 1 indicating strongly disagree, indicating disagree, 3 indicating agree and and 4 indicating strongly agree). They received average rating of 3.76 indicating high quality.
A.3.3 TRAINING DETAILS
We use the LORA Hu et al. 2021 training recipe published in the LLaVA repository 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. 2017) and enabling training each variant on 4 NVIDIA A100 GPUs in single day.
A.4 MLLAVA PROBING
A.4.1 THE LOGIT LENS
The logit lens is a tool from mechanistic interpretability used to probe LMs Nostalgebraiist 2020). It works by applying the LM's "unembedding matrix" directly to hidden states from each layer of the model, projecting each hidden state to a distribution over the model's output vocabulary. By decoding LM's hidden states in this manner, we get intrinsic insight into how model converges
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Published as a conference paper at ICLR 2025
to its final output distribution through iterative updates to token probabilities. Wendler et al. (2024) make us of this technique in probing the internals of Llama2 when generating non-English text to make the case that the model is "operating" in English. Here, perform similar analysis of our Baichuan2 and Llama2-based VLMS to probe how each VLM handles the same input images.
A.4.2 PROBING DETAILS
To create F for a particular mLLa VA variant, we embed every image in ArtELingo's 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, 2020); we consider only the tokens with decoded probability greater than 40%. This results in set of subword tokens for each image & layer. We map these tokens to the most frequent word on Wikipedia that contains that toker producing a list of words (in English and Chinese) for each layer. For given layer, count the number of English and Chinese words across all images to produce the "Lang Token Counts" in Figure 6
Then, for each image, use XLM-R (Conneau al. 2019) compute similarities between each of its decoded words at 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 6
We include the raw English and Chinese token counts and rationale similarity ratios at each layer in Table 4 A similarity greater than 1 indicates that the tokens at that layer were more similar to the English rationales (and less than 1, more similar to the Chinese rationales).
Using
frequency
lists
from
https://gi thtp:///tthuu..oooo wttp://cccmmyyysemrroo...o
wi k pedia ford-frequens
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Published as a conference paper at ICLR 2025
Table 4: Probing results. For each layer and model, "en" indicates the number of decoded English tokens, "zh" indicates the number of decoded Chinese tokens and "sim" indicates the average token similarity with English rationales divided by the average token similarity with Chinese rationales.
Ilama2 (en)
Ilama2 (zh)
baichuan2 (en)
baichuan2 (zh)
Layer
en
zh
sim
en
zh
sim
en
zh
sim
en
zh
sim
72
2.45
52
1.34
0
2
69
0
2.65
107
0
1.61
0
0
0
3
74
2.87
59
1.54
0
0
4
51
0
3.55
66
0
1.33
0
O
0
5
1.87
59
1.27
0
0
6
148
1.67
98
0
1.28
0
0
0
7
162
1.23
157
1.39
0
0
8
298
0.96
308
0
1.36
0
0
9
364
1.11
2671
0
1.44
0
10
542
1.21
3743
0
1.24
11
704
1.15
4387
0
1.34
0
5.16
12
982
1.27
3969
1.27
1.67
13
1645
2.50
3906
O
1.22
15
0
2.58
0
14
2352
1.16
3536
1.15
0
2.26
0.88
15
2472
1.31
5721
O
1.50
6
0
1.09
3
0
0.89
16
4613
1.72
6798
1.43
0
1.68
0
1.55
17
7541
1.93
9277
O
1.78
136
0
1.67
68
0
1.89
18
11298
1.58
11424
1.55
382
0
1.35
218
1
2.02
19
19579
1.55
1 17552
O
1.46
590
0
1.42
361
2
1.61
20
32499
1.51
25341
1.34
1641
21
1.10
983
14
1.40
21
44263
11
1.49
27854
5
1.33
5782
120
1.23
4811
237
1.50
22
64210
1.44
42098
0
1.37
13039
80
1.29
8514
1155
1.39
23
73047
10
1.44
45719
1.32
19733
50
1.23
10185
1833
1.34
24
76269
1.42
44809
0
1.30
26892
19
1.22
11361
4925
1.29
25
79960
1.41
49209
0
1.35
37796
5
1.21
16173
6569
1.26
26
75188
1.36
46590
0
1.32
46871
1
1.21
17342
10140
1.23
27
76809
1.39
48820
1.36
44590
0
1.20
15195
8659
1.23
28
73872
1.35
42260
4
1.27
47456
0
1.20
15377
9684
1.22
29
77020
1.39
41425
1.28
39814
0
1.16
12220
7421
1.22
30
77346
1.48
45040
3
1.46
34484
0
1.14
7454
4578
1.13
31
84294
1.57
46830
1.51
28616
0
1.16
3838
2328
1.11
32
100456
1.80
41390
1
1.62
32156
0
1.18
3378
2591
1.13
33
74435
1.78
51387
135
1.76
70039
0
1.51
22363
12905
1.30
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Published as a conference paper at ICLR 2025
Table 5: The scores, biases and bias reductions of our off-the- thef- (OTS) VLMs on the Western (W) and East Asian (E) splits of our selected Dollar Street (DS), VQA and ArtELingo (Art) subsets. Note that as blip2-flan-t5-xx does not generate fluent Chinese, we do not evaluate it in that setting.
Prompt in en
Prompt in zh
Model
Bias Reduction
W
E
Bias
W
E
Bias
blip2-flan-t5-xxl
0.716
0.654
1.094
DSS en/zh
1lava-1.5-7b
0.728
0.612
1.189
0.541
0.492
1.101
0.088
Ilava-..5-mistral776
0.824
0.729
1.130
0.007
0.006
1.156
-0.026
mblip-bloomz--7
0.638
0.578
1.103
0.513
0.479
1.071
0.032
qwen-vl-7b
0.700
0.608
1.151
0.516
0.541
0.953
0.198
qwen-vl-chat-7b
0.700
0.624
1.122
0.616
0.533
1.155
0.034
blip2-flan-t5-xx
0.681
0.620
1.097
DS: region
1l5va-1.5-7b
0.692
0.602
1.149
0.528
0.470
1.123
0.026
Ilava-1.5-mistral177
0.803
0.701
1.147
0.004
0.004
0.897
0.250
mblip-bloomz--7
0.615
0.551
1.116
0.494
0.451
1.095
0.021
qwen-vl-7b
0.666
0.577
1.154
0.494
0.497
0.993
0.162
qwen-vl-chat-7b
0.656
0.591
1.111
0.605
0.499
1.214
-0.144
blip2-flan-t5-xxl
0.686
0.553
1.240
1lava-1.5-7b
0.371
0.367
1.013
0.507
0.383
1.324
0.311
VQA:
Ilava-1.5-mistral-77b
0.491
0.463
1.060
0.521
0.392
1.329
0.269
mblip-bloomz--7
0.605
0.402
1.506
0.536
0.424
1.263
0.242
qwen-vl-7b
0.762
0.621
1.227
0.632
0.592
1.069
0.158
qwen-vl-chat-7b
0.774
0,611
1.267
0.631
0.566
1.116
0.151
blip2-flan-tt-xxx
0.580
0.488
1.187
VQA: region
1lava-1.5-7b
0.361
0.337
1.073
0.412
0.312
1.321
0.248
llava-l.5mmistral-77
0.448
0.410
1.092
0.436
0.356
1.226
0.134
mblip-bloomzz-7
0.485
0.346
1.402
0.450
0.358
1.260
0.142
qwen-vl-7b
0.655
0.527
1.244
0.533
0.473
1.126
0.118
qwen-vl-chat-7b
0.648
0.521
1.245
0.514
0.444
1.158
0.087
blip2-flan-t5-xxx
0.220
0.111
1.983
Art: opp. val.
1lava-1.5-7b
0.182
0.145
1.252
0.081
0.064
1.270
0.018
llava-l.5-mistral777
0.193
0.152
1.272
0.082
0.073
1.114
0.158
mblip-bloomz-76
0.098
0.100
0.980
0.074
0.071
1.049
0.068
qwen-vl-7b
0.179
0.160
1.118
0.089
0.079
1.131
0.013
qwen-vl-chat-7b
0.217
0.152
1.426
0.083
0.084
0.998
0.428
blip2-flan-t5-xxl
0.268
0.261
1.027
1lava-1.5-7b
0.269
0.262
1.025
0.036
0.033
1.087
0.062
Art: all
Ilava-1.5-mistral777
0.312
0.257
1.213
0.044
0.036
1.229
-0.016
mblip-bloomz-76
0.160
0.166
0.962
0.059
0.035
1.696
00.34
qwen-vl-7b
0.267
0.255
1.048
0.054
0.042
1.285
-0.336
qwen-vl-chat-7b
0.266
0.247
1.079
0.059
0.031
1.914
-0.835
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Published as a conference paper at ICLR 2025
Table 6: The accuracy, Western bias and bias reductions of our mLLaVA variants. M indicates the base LLM (L: the mostly English Llama2, B: the English/Chinese Baichuan2); its subscript indicates the fusion corpus (m: monolingual, bi: bilingual). I, the prompting language. W and E, the accuracy on the Western and East Asian splits. Bias, the Western bias (West / East Asian). Atims Stang and fus indicate the change Western bias when changing the base LLM Baichuan2, the prompting language to Chinese or the fusion corpus to bilingual. lists p-values calculated using a two-factor design (Krzywinski 2014). Bolded entries are significant at the 0.05 level.
M
L
W
E
Bias
Allm
p
A Long
p
A fas
p
Dollar Street: en/zh
Ly
0.812
0.721
1.126
0.608
0.529
1.149
-0.023
0.676
0.757
0.705
1.074
0.052
0.143
Bm
zh
0.697
0.585
1.190
-0.041
0.274
-0.116
0.035
0.825
0.743
1.110
0.016
0.722
Lbi
zh 0.594
0.538
1.103
0.046
0.433
0.739
0.657
1.125
-0.051
0.278
Bbi
zh
0.652
0.590
1.106
0.085
0.094
Dollar Street: region
Lm
0.782
0.702
1.114
zh
0.597
0.523
1.142
-0.028
0.740
0.743
0.688
1.080
0.034
0.147
Bm
zh 0.654
0.564
1.159
-0.017
0.416
-0.099
0.059
0.802
0.715
1.121
-0.006
0.715
Lic
zh 0.595
0.527
1.130
0.012
0.751
en 0.728
0.640
1.138
0.057
0.067
Bb zh 0.646
0.555
1.163
-0.04
0.975
0.644
0.434
1.483
Lm
zh 0.510
0.341
1.496
-0.013
0.361
VQA: en/zh
0.620
0.428
1.450
0.033
0.696
B zh 0.555
0.476
1.165
0.331
0.044
0.285
0.011
en 0.521
0.363
1.435
0.048
0.244
Lx
zh 0.485
0.305
1.587
-0.90
0.822
en 0.565
0.383
1.477
0.027
0.822
Bbi zh 0.519
0.434
1.195
0.030
0.894
0.515
0.407
2666
Lm zh 0.412
0.320
1.288
-0.022
0.518
VQA: region
0.494
0.389
1,270
-0.05
0.908
B
zh 0.448
0.387
1.159
0.129
0.218
0.112
0.082
en 0.440
0.350
1.256
0.010
0.461
Lii
zh 0.390
0.304
1.284
0.004
0.809
en 0.472
0.383
1.234
0.037
0.532
Bbi
zh 0.432
0.379
1.139
0.012
0.727
ArtEl Lingo: opp. val.
Lm zh 0.207
en 0.249
0.072
3.448
0.084
2.472
0.976
0.000
en 0.241
0.089
2.715
0.733
0.081
zh 0.306
0.249
1.228
1.244
0.000
1.487
0.000
en 0.220
0.030
4.389
0.941
0.625
Lix
zh 0.185
0.087
2.124
0.349
0.055
en 0.221
0.040
5.505
-2.700
0.032
B
zh 0.288
0.242
1.190
0.039
0.526
en 0.292
0.133
2.195
ArtELinge Lngg: all
zh 0.233
0.128
1.822
0.373
0.000
en 0.291
0.147
1.981
0.213
0.000
B
zh 0.367
0.318
1.156
0.666
0.000
0.825
0.000
en 0.245
0.092
2.647
-0.452
0.076
Lbc
zh 0.240
0.171
1.401
0.421
0.000
en 0.219
0.065
3.393
11.41
0.004
Bыg zh 0.267
0.227
1.178
0.022
0.053
22


Reviews:
[{"review_id": "ZnhzqKYTaX", "reviewer": "Reviewer_WwFh", "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.", "questions": "N/a", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper has some important strengths that I list below:\n\n1. 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\n\n2. 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.", "weaknesses": "Despite the strengths highlighted, the current paper falls short in certain aspects that I highlight below:\n\n1. 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.\n\n2. 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.\n\n3. 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?).\n\n4. 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", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WnbyxFzPGc", "reviewer": "Reviewer_bfSC", "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.", "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?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 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.", "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. \n\nThe 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. \n\nIt 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. \n\n[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.\n\nSome additional details would be beneficial:\n\nSection 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. \n\nThe 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.  \n\nPrompting 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JRfcyd1Mq5", "reviewer": "Reviewer_Z8Wd", "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).", "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].\n\n---\n[3] Gu et al. Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark. NeurIPS 2022.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 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.\n2. The results corroborate the findings that earlier adoption of multilingual data in LLMs consistently benefits upstream VLMs.\n3. 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.", "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.\n2. 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.\n3. 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.\n4. Nit: Please make text in Figures much bigger.\n\n---\n[1] Qiu et al. Multilingual Multimodal Learning with Machine Translated Text. EMNLP 2022.\n\n[2] Ramos et al. PAELLA: Parameter-Efficient Lightweight Language-Agnostic Captioning Model. NAACL 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "e4LKALkBVs", "reviewer": "Reviewer_pN9q", "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.", "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?\n* 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)?\n\nI look forward to an active discussion with the authors during the rebuttal phase and will revise my score accordingly.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper discusses the cultural bias in VLMs, providing a comprehensive analysis of how language influences this bias in VLMs.\n* The paper offers a comparative analysis between English and Chinese, two major languages, which provides valuable insights into bias reduction.\n* The paper presents a comprehensive methodology, with controlled experiments to trace bias sources and measure the effects of different language modeling choices on bias.", "weaknesses": "* The study's focus on English and Chinese may limit the generalizability of the findings to other languages and cultures.\n* 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)