Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts
- URL: http://arxiv.org/abs/2406.17974v1
- Date: Tue, 25 Jun 2024 23:11:39 GMT
- Title: Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts
- Authors: Xuyang Wu, Yuan Wang, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang,
- Abstract summary: We empirically investigate emphvisual fairness in several mainstream vision-language models (LVLMs)
We audit their performance disparities across sensitive demographic attributes based on public fairness benchmark datasets (e.g., FACET)
Despite enhancements in visual understanding, both open-source and closed-source LVLMs exhibit prevalent fairness issues across different instruct prompts and demographic attributes.
- Score: 27.66626125248612
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large vision-language models (LVLMs) have recently achieved significant progress, demonstrating strong capabilities in open-world visual understanding. However, it is not yet clear how LVLMs address demographic biases in real life, especially the disparities across attributes such as gender, skin tone, and age. In this paper, we empirically investigate \emph{visual fairness} in several mainstream LVLMs and audit their performance disparities across sensitive demographic attributes, based on public fairness benchmark datasets (e.g., FACET). To disclose the visual bias in LVLMs, we design a fairness evaluation framework with direct questions and single-choice question-instructed prompts on visual question-answering/classification tasks. The zero-shot prompting results indicate that, despite enhancements in visual understanding, both open-source and closed-source LVLMs exhibit prevalent fairness issues across different instruct prompts and demographic attributes.
Related papers
- GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing [72.0343083866144]
This paper introduces the GenderBias-emphVL benchmark to evaluate occupation-related gender bias in Large Vision-Language Models.
Using our benchmark, we extensively evaluate 15 commonly used open-source LVLMs and state-of-the-art commercial APIs.
Our findings reveal widespread gender biases in existing LVLMs.
arXiv Detail & Related papers (2024-06-30T05:55:15Z) - Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals [8.41410889524315]
We study the social biases contained in text generated by Large Vision-Language Models (LVLMs)
We present LVLMs with identical open-ended text prompts while conditioning on images from different counterfactual sets.
We evaluate the text produced by different models under this counterfactual generation setting at scale, producing over 57 million responses from popular LVLMs.
arXiv Detail & Related papers (2024-05-30T15:27:56Z) - VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models [57.43276586087863]
Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs.
Existing benchmarks are often limited in scope, focusing mainly on object hallucinations.
We introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases.
arXiv Detail & Related papers (2024-04-22T04:49:22Z) - Debiasing Multimodal Large Language Models [61.6896704217147]
Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing.
Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image.
To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
arXiv Detail & Related papers (2024-03-08T12:35:07Z) - Finer: Investigating and Enhancing Fine-Grained Visual Concept
Recognition in Large Vision Language Models [68.46457611340097]
In-depth analyses show that instruction-tuned LVLMs exhibit modality gap, showing discrepancy when given textual and visual inputs that correspond to the same concept.
We propose a multiple attribute-centric evaluation benchmark, Finer, to evaluate LVLMs' fine-grained visual comprehension ability and provide significantly improved explainability.
arXiv Detail & Related papers (2024-02-26T05:43:51Z) - Good Questions Help Zero-Shot Image Reasoning [110.1671684828904]
Question-Driven Visual Exploration (QVix) is a novel prompting strategy that enhances the exploratory capabilities of large vision-language models (LVLMs)
QVix enables a wider exploration of visual scenes, improving the LVLMs' reasoning accuracy and depth in tasks such as visual question answering and visual entailment.
Our evaluations on various challenging zero-shot vision-language benchmarks, including ScienceQA and fine-grained visual classification, demonstrate that QVix significantly outperforms existing methods.
arXiv Detail & Related papers (2023-12-04T03:18:51Z) - Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models [50.653838482083614]
This paper introduces a scalable test-bed to assess the capabilities of IT-LVLMs on fundamental computer vision tasks.
MERLIM contains over 300K image-question pairs and has a strong focus on detecting cross-modal "hallucination" events in IT-LVLMs.
arXiv Detail & Related papers (2023-12-03T16:39:36Z) - Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity [45.86789047206224]
This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition.
Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity.
Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data.
arXiv Detail & Related papers (2023-06-28T09:29:06Z)
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.