See It from My Perspective: Diagnosing the Western Cultural Bias of Large Vision-Language Models in Image Understanding
- URL: http://arxiv.org/abs/2406.11665v1
- Date: Mon, 17 Jun 2024 15:49:51 GMT
- Title: See It from My Perspective: Diagnosing the Western Cultural Bias of Large Vision-Language Models in Image Understanding
- Authors: Amith Ananthram, Elias Stengel-Eskin, Carl Vondrick, Mohit Bansal, Kathleen McKeown,
- Abstract summary: Vision-language models (VLMs) can respond to queries about images in many languages.
We present a novel investigation that demonstrates and localizes Western bias in image understanding.
- Score: 78.88461026069862
- License: http://creativecommons.org/licenses/by/4.0/
- 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. 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.
Related papers
- Analyzing Cultural Representations of Emotions in LLMs through Mixed Emotion Survey [2.9213203896291766]
This study focuses on analyzing the cultural representations of emotions in Large Language Models (LLMs)
Our methodology is based on the studies of Miyamoto et al. (2010), which identified distinctive emotional indicators in Japanese and American human responses.
We find that models have limited alignment with the evidence in the literature.
arXiv Detail & Related papers (2024-08-04T20:56:05Z) - Vision-Language Models under Cultural and Inclusive Considerations [53.614528867159706]
Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives.
Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case.
We create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind.
We then evaluate several VLMs, investigating their reliability as visual assistants in a culturally diverse setting.
arXiv Detail & Related papers (2024-07-08T17:50:00Z) - CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation [49.41531871253317]
We present a new Chinese Vision- Language Understanding Evaluation benchmark dataset.
The selection of object categories and images is entirely driven by Chinese native speakers.
We find that fine-tuning on Chinese culture-related VL datasets effectively enhances VLMs' understanding of Chinese culture.
arXiv Detail & Related papers (2024-07-01T08:35:37Z) - CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark [68.21939124278065]
Culturally-diverse multilingual Visual Question Answering benchmark designed to cover a rich set of languages and cultures.
CVQA includes culturally-driven images and questions from across 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k questions.
We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models.
arXiv Detail & Related papers (2024-06-10T01:59:00Z) - An Introduction to Vision-Language Modeling [128.6223984157515]
The vision-language model (VLM) applications will significantly impact our relationship with technology.
We introduce what VLMs are, how they work, and how to train them.
Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
arXiv Detail & Related papers (2024-05-27T15:01:23Z) - Computer Vision Datasets and Models Exhibit Cultural and Linguistic
Diversity in Perception [28.716435050743957]
We study how people from different cultural backgrounds observe vastly different concepts even when viewing the same visual stimuli.
By comparing textual descriptions generated across 7 languages for the same images, we find significant differences in the semantic content and linguistic expression.
Our work points towards the need to accounttuning for and embrace the diversity of human perception in the computer vision community.
arXiv Detail & Related papers (2023-10-22T16:51:42Z) - Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in
Large Language Models [89.94270049334479]
This paper identifies a cultural dominance issue within large language models (LLMs)
LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
arXiv Detail & Related papers (2023-10-19T05:38:23Z) - Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times
and Location Reasoning [23.33600235294496]
Vision-Language Models (VLMs) are expected to be capable of reasoning with commonsense knowledge as human beings.
This makes us wonder if, based on visual cues, Vision-Language Models can achieve and even outperform human's capability in reasoning times and location.
We propose a two-stage recognitionspace and reasoningspace probing task, applied to discriminative and generative VLMs.
arXiv Detail & Related papers (2023-07-12T13:46:28Z) - Having Beer after Prayer? Measuring Cultural Bias in Large Language Models [25.722262209465846]
We show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture.
We introduce CAMeL, a novel resource of 628 naturally-occurring prompts and 20,368 entities spanning eight types that contrast Arab and Western cultures.
Using CAMeL, we examine the cross-cultural performance in Arabic of 16 different LMs on tasks such as story generation, NER, and sentiment analysis.
arXiv Detail & Related papers (2023-05-23T18:27:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.