# Data Processing Techniques for Modern Multimodal Models

Yinheng Li1 Columbia University
New York, NY, USA
<EMAIL_ADDRESS>Han Ding1 Columbia University
New York, NY, USA
<EMAIL_ADDRESS>Hang Chen New York University
New York, NY, USA
<EMAIL_ADDRESS>missing

###### Abstract

Data processing plays an significant role in current multimodal model
training. In this paper. we provide an comprehensive review of common data
processing techniques used in modern multimodal model training with a focus on
diffusion models and multimodal large language models (MLLMs). We summarized
all techniques into four categories: data quality, data quantity, data
distribution and data safety. We further present our findings in the choice of
data process methods in different type of models. This study aims to provide
guidance to multimodal models developers with effective data processing
techniques.

###### Index Terms:

Data Processing, Data Augmentation, Stable Diffusion, Multimodal Large
Language Model, Bias and Fairness

11footnotetext: These authors contributed equally to this work. The order is
completely random.

##  I Introduction

The recent success of multimodal models heavily relies on the use of large-
scale datasets such as LAION-5B [1], Conceptual Caption-3M [2]. In addition to
annotated and curated datasets, web-crawled data has increasingly played a
significant role in training datasets. The first step of model training is
usually data collection and data cleaning. Having a high quality dataset has
proven to be essential for developing robust and high-performing models [3].

In this work, we surveyed a list of influential multimodal model research at
the time of this paper. We review and categorize the commonly used data
processing methods and summarize a standard data processing framework for
training multimodal models. This framework addresses topics such as data
quality, data distribution, and data safety. We believe this work will provide
valuable guidance and insights for future efforts to train or fine-tune
multimodal models.

##  II Background

####  II-1 Multimodal Generative models

Multimodal models have seen significant development in recent years. While
multimodal models often refer to those that incorporate more than one
modality, vision-language models are the most extensively studied areas [4,
5]. In this work, we focus on data processing techniques for vision-language
models, and some of these concepts can be also applied to models involving
other modalities.

There are two major categories of vision-language models: discriminative
models and generative models. Discriminative models typically use early fusion
or late fusion architectures to learn meaningful representations for
downstream tasks such as classification and ranking. Most early work centered
on discriminative models, including ALBEF [6], ALIGN [7], and CLIP [8].

Generative models, on the other hand, aim to generate text or image outputs.
Generative vision-language models can be further divided into image generation
models (e.g., diffusion models) [9] and text generation models (e.g., MLLMs)
[10]. Diffusion models, for instance, utilize U-Net [11] and transformer
architectures [12] for text-based image generation. MLLMs have emerged
following the introduction of large language models (LLMs) [13]. With the
success of LLMs, researchers have explored the possibility of incorporating
visual information as tokens into LLM models to leverage the previously
learned knowledge in LLMs. Influential works in this area include LLaVA [14]
and miniGPT-4 [15].

In this work, we will focus on the data processing techniques used in
diffusion models and MLLMs, because these models represent the forefront of
multimodal machine learning, with significant advancements and wide-ranging
applications. While our mainly focus on these recent models, we also cover
some early discriminative models to provide a comprehensive overview of data
processing techniques in the field.

####  II-2 Multimodal Dataset

Training datasets play a critical role in both model pretraining and
finetuning. A typical dataset for vision-language models consists of pairs of
images and text. The text and image pairs can have various relationships,
corresponding to different tasks such as visual question answering (VQA) [16],
image retrieval [17], and text-to-image generation [9]. The most commonly used
dataset for pretraining vision-language models is the image captioning
dataset, where the text is a description of the image. Examples include
Conceptual Captions [2] and WIT [18].

Most of these data are collected from the internet but require significant
processing. Recently, there has been a considerable effort in collecting and
open-sourcing larger datasets, such as LAION-5B. The increase in both the
scale and quality of training datasets has been observed to correlate with the
improvement of vision-language models.

##  III Data Processing techniques

Figure 1: Data Processing Workflow

In this section, we discuss important data processing techniques used in
multimodal generative models. To obtain a ”good” training dataset, most
efforts have focused on improving data distribution, data safety, data
quality, and data quantity. We categorize the data processing techniques
accordingly. In this work, we focus on three out of the four categories: data
quality, data distribution, and data safety. Although data quantity is an
important factor, it is not covered in this work as it relates to data
sources, which are highly dependent on specific use cases.

We have outlined a standard workflow for data processing in training
multimodal models in Figure 1. Generally, there are two types of data for used
for training multimodal model: raw data and curated data. Raw data, such as
LAION-5B, is collected directly from the internet and often comes in large
quantities. However, it requires extensive cleaning to ensure high quality,
safety, and usefulness for model training. On the other hand, curated
datasets, such as MMC4 [19], are human-curated and require less effort in data
cleaning, such as removing unsafe content. Nevertheless, it can still be
beneficial to further improve data quality or optimize data distribution for
the downstream task.

Once data is collected, we move to the data processing stage, where various
techniques are used to enhance data quality, ensure data safety, and optimize
or balance data distribution. The processed data is then used for model
training. It is important to note that this is an iterative process. After the
model is trained, we evaluate its performance and analyze failure cases. If
certain issues are identified as caused by flaws in the training data, we
return to the data processing step to adjust our methods. Additionally, if the
data becomes insufficient at any point, we return to the initial stage to find
more data sources and collect additional data.

In the following sections, we present a comprehensive collection of popular
methods for quality filtering, distribution balancing, and data safety.

###  III-A Data Quality

Data quality has always been a crucial factor in model training[20].
Significant efforts have been made to improve the quality of single-modal
data, such as WebText [21]. In the multimodal setting, it is essential not
only to enhance the quality of each individual modality but also to improve
the alignment between image and text pairs. Most techniques for improving
training data quality fall into two categories: data filtering and data
augmentation.

####  III-A1 Filtering

Quality filtering focuses on individual data samples, applying criteria to
filter based on specific attributes. This can be done for text and images
data, either separately or together.

Image Filter Image filters are most commonly used in diffusion based model
training.

  1. 1.

Image property filter: Image resolution is a crucial aspect of quality; images
with lower resolution generally exhibit lower quality and less information. A
resolution filter is widely used in both MLLMs and diffusion-based models.
Notable works like Stable Diffusion and EMU [22, 23] used resolution filters
to process training datasets, retaining only images above a certain resolution
threshold. Similarly, BLIP [24] applies resolution filters to exclude low-
quality images.

Aspect ratio is another important factor. Images with extreme aspect ratios
are difficult to process into the square shape which are typically required by
training functions. Deepfloyd[25] and EMU[22] filter their training images
based on aspect ratio. SDXL [23] also emphasizes the importance of aspect
ratio for improving model performance.

  2. 2.

Advanced filter: In addition to filters on the intrinsic properties on an
image, filters can be defined based on abstract and advanced concepts.

Annotator Guided Filter: Some models use expert annotators to filter data
based on aesthetic quality. EMU, for instance, relies on annotators with
photography expertise to select the highest quality images, which are then
used to fine-tune the model. This results in generated images that are
significantly preferred for their visual appeal and text faithfulness.

Model Guided Filter: Other works, like Stable Diffusion1.5 [22] and Stable
Diffusion2.1[26], use smaller models to score each image and filter them based
on these scores. These scores can represent aesthetic quality or watermark
intensity.

  3. 3.

Source filter: The data source can indicate the quality of the data. For
example, images from social platforms (e.g., Instagram) often come with user
feedback such as likes or reposts, which can indicate the quality of the image
from the user’s perspective. EMU uses the number of likes on an image as a
filter to select high-quality images.

Text Filter Text filtering is more common in MLLM-related research.

  1. 1.

Annotator Guided Filter: Annotator guided filters are also common in text
filtering. In miniGPT-4 [15], training data for the second stage is filtered
and refined by humans with assistance from ChatGPT.

  2. 2.

Model Guided Filter: In miniGPT-4, ChatGPT is used as an initial filter to
detect errors in image captions. The authors define frequent errors in the
captions and instruct ChatGPT to identify such errors in the dataset. In BLIP
[24], a model is trained specifically to filter out captions that are not
well-aligned with the given image.

  3. 3.

Image-Text Alignment Filter: Multimodal alignment filtering is also important.
CLIP [8] models are widely used to measure alignment across different
modalities, especially for image and text. In Stable Diffusion, SDXL, and EMU,
image-text pairs are filtered based on their CLIP scores, improving the
model’s capability of following user instructions.

TABLE I: Data Processing Techniques and Datasets in Different Models Model |  Model Category |  Safety |  Quality |  Balance |  Pretraining Dataset |  Finetuning Dataset
---|---|---|---|---|---|---
LLaVA |  MLLM |  - |  - |  Noun Phrase Popularity, use ChatGPT to generate Instruct-158K dataset for finetuning |  Conceptual Captions |  Instruct-158K, ScienceQA [27]
LLaVA-1.5 |  MLLM |  - |  - |  Unknown |  OKVQA[28], A-OKVQA[29] , OCRVQA[30], TextCaps[31], Visual Genome, RefCOCO[32], ShareGPT |  Downstream tasks
MiniGPT4 |  MLLM |  - |  Use early model to generate data, then use ChatGPT and human filtering to get 3,500 image-text pairs |  - |  Conceptual Captions, SBU[33], LAION |  3.5k image-text pairs
CLIP |  Dual Encoder |  Bias toward age, gender, and race is reported, no mitigation is mentioned |  Unknown |  Noun Phrase Popularity |  Closed Source Dataset |  -
BLIP |  MLLM |  N/A |  Model guided filtering and generation to get CapFilt dataset |  - |  COCO, Visual Genome[34], Conceptual Captions, Conceptual 12M [35], SBU |  CapFilt dataset
BLIP-2 |  MLLM |  N/A |  Model guided filtering and generation to get CapFilt, use CLIP to filter image-text aligned data |  - |  COCO, Visual Genome[34], Conceptual Captions, Conceptual 12M [35], SBU, LAION-400M |  Downstream tasks
Stable Diffusion 1.5 |  Diffusion Models |  NSFW Filter, Watermark Filter, Image-Text Alignment |  Resolution, Aesthetic |  - |  LAION-5B |  -
Stable Diffusion 2.1 |  Diffusion Models |  NSFW Filter, Watermark Filter |  Resolution, Aesthetic, Image-Text Alignment |  - |  LAION-5B |  -
Stable Diffusion XL |  Diffusion Models |  NSFW Filter, Watermark Filter, Image-Text Alignment |  Unknown |  Unknown |  Private Dataset |  -
EMU |  Diffusion Models |  NSFW Filter |  Resolution, Aesthetic, Aspect Ratio, Source Filter |  Image classification |  Private Dataset |  Closed Source Dataset
Deep Floyd |  Diffusion Models |  NSFW Filter |  Resolution, Aesthetic, Aspect Ratio |  - |  LAION-5B |  -

####  III-A2 Augmentation

With the advancement of vision-language models, it has become feasible to
generate high-quality text and image data using early model versions or
closed-source APIs (ChatGPT). These generated data can be used to further
train and improve models.

Many works in this field focuses on generating high-quality text captions for
images. For instance, BLIP [24] uses captioning models initialized from early
BLIP model checkpoints to generate captions for web images. Similarly,
MiniGPT-4 [15] employs early checkpoints to generate descriptions for
finetuning datasets, with ChatGPT-4 [36] further refining these captions. In
the training of LLaVA-1.5 [14], large-scale GPT-annotated datasets such as
LAION-GPT-V and ShareGPT-4V [37] are utilized to enhance performance.

###  III-B Data Distribution

Distribution balancing focuses on producing a well-balanced and diverse
dataset based on predefined criteria. Data sampling is widely used in
multimodal data processing. While typically used for filtering higher quality
data, it is also useful for balancing the training data distribution. Text and
images are the two primary data sources for vision-language models,
corresponding to two dimensions for filtering and balancing.

####  III-B1 Image Oriented Balancing

In the EMU model [38], images are balanced by leveraging visual concepts from
different domains using an image classification model [39]. Image
deduplication is another common practice in data processing. In the MMC4 [19]
dataset, models like phash [40], which identify visually similar images, are
used to remove duplicates. The MMC4 dataset is popular for developing vision-
language models such as OpenFlamingo [41].

####  III-B2 Text Oriented Balancing

Text-oriented data balancing is more common in MLLMs. In LLaVA, image captions
are sampled based on noun phrase frequency. The goal is to exclude rare noun
phrases and reduce overly common ones. For a text caption to be included, the
frequency must be at least three. Meanwhile, if a noun phrase appears too
frequently (more than 100 times), only 100 captions containing that phrase are
randomly selected. In addition to sampling, LLaVA model uses GPT-4 and ChatGPT
to generate in-depth and diversity conversations from COCO images[42]. A
similar approach has been found in creating pretraining dataset for CLIP[8]
model. To ensure a coverage of a broad set of visual concepts, the authors use
frequently occurring words from Wikipedia to construct base queries. These
queries filter text captions, ensuring that only those including one of the
query words are included in the dataset.

###  III-C Data Safety

Data safety is critical in developing ethical and trustworthy models. In this
section, we define two problems of data safety: data toxicity and social bias
and unfairness. While there is no consensus definition of toxic data, it
generally includes violent, pornographic, offensive, and unethical content,
also known as Not Safe For Work (NSFW). Biased and unfair data are more subtle
and refer to social biases or stereotypes in the data. The definition of bias
and fairness is more ambiguous than NSFW data, as it often depends on specific
downstream tasks and cultural background.

####  III-C1 Toxic Data

For multimodal datasets, toxic data can appear in both image and text content.
Wordlists such as LDNOOBW [43] are created to filter harmful text. For image
safety, CLIP embeddings are often used to compare images with a list of toxic
text, and images with high similarity scores are tagged as unsafe. For
instance, in the LAION-5B dataset, the authors use both the Q16 classifier
[44] and their own specialized pornographic and sexual content classifier,
both based on CLIP embeddings, to filter inappropriate images. Stable
Diffusion 1.5 and 2.1 use this LAION dataset with the NSFW filter.

####  III-C2 Social Bias and Fairness

Addressing bias and fairness in MLLMs is an emerging research area. a number
of work has been done to analyze and evaluate social bias in language models
[45, 46, 47], and similar techniques can be applied to processing text in
multimodal training data. Counterfactual data augmentation (CAD) is a common
technique used to balance representation bias in text data. For example, in
[48], CAD is used to balance bias by flipping gendered pronouns in existing
data to generate new samples. In diffusion models, social bias and stereotypes
have been recognized as problems [49, 50, 51]. However, most debiasing work is
focused on model training rather than data processing [52].

To summarize, while eliminating harmful content for data safety has been
largely successful, more work is needed to mitigate social bias and promote
fairness in multimodal models.

##  IV Comparison of Data Processing Techniques in Different Models

Table I provides a summary of the MLLMs and diffusion models discussed in the
previous section. It provides a clear comparison of the techniques used in
different types of models.

According to Table I, diffusion models focus more on image data quality, such
as aesthetics and resolution, while MLLMs focus more on text data quality and
its alignment with images. Diffusion models often require large scale data
such as LAION-5B, where data safety filters are applied to remove toxic data.
MLLMs frequently use curated datasets which is smaller and require fewer
additional safety handling in data processing. This is because the LLM model
inside MLLMs have already undergo training with large scale text data. So, a
majority of the work in MLLM is only to properly project image into text
tokens. Both diffusion models and MLLMs leverages distribution balancing
techniques to obtain a representative and diverse dataset in terms of text or
image topics.

Data augmentation has become a popular technique for generating high-quality
data for model training, especially for MLLMs. Since MLLMs leverage existing
LLM frameworks such as LLaMA [53], which is equipped substantial capabilities
in understanding and generating text, these models do not require a highly
diverse set of text. However, for the model to understand the association
between text and image, it is crucial that the text is well-aligned with the
image. Text alignment is a core aspect of data quality in MLLM training. It
has been shown, in the training of models like MiniGPT-4 and LLaVA-next, that
synthetic data generated from LLMs or early checkpoints results in better text
alignment than unprocessed raw data. For diffusion models, using synthetic
image-text data for training is less common.

In the data processing techniques used by both diffusion models and MLLMs,
there is a trend toward increased use of model-based filters, such as using
ChatGPT or NSFW classifiers to refine training data. However, human annotators
are still used as the final judges of data quality. Especially during the
finetuning stage, which requires extremely high-quality data, the inclusion of
human expert annotators can still lead to significant improvements in data
quality.

##  V Conclusion

In this paper, we discussed common data processing techniques for multimodal
models, specifically vision-language models. We proposed a framework that
categorizes these techniques from four perspectives: quantity, quality,
safety, and distribution. We compared the techniques used in image generation
models—diffusion models—and text generation models—MLLMs.

Data processing is an iterative process. The choice of processing methods
depends on specific tasks as well as model performance, and there are no
standard steps to follow for training data processing. While this survey does
not provide an exhaustive list of all available data processing techniques, we
have carefully selected the techniques used by the most influential models. We
hope this work provides a useful guideline for deciding on methods for data
processing.

## References

  * [1] C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman _et al._ , “Laion-5b: An open large-scale dataset for training next generation image-text models,” _Advances in Neural Information Processing Systems_ , vol. 35, pp. 25 278–25 294, 2022.
  * [2] P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in _Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , I. Gurevych and Y. Miyao, Eds. Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 2556–2565. [Online]. Available: https://aclanthology.org/P18-1238
  * [3] L. Budach, M. Feuerpfeil, N. Ihde, A. Nathansen, N. Noack, H. Patzlaff, F. Naumann, and H. Harmouch, “The effects of data quality on machine learning performance,” 2022.
  * [4] Y. Du, Z. Liu, J. Li, and W. X. Zhao, “A survey of vision-language pre-trained models,” 2022.
  * [5] J. Duan, L. Chen, S. Tran, J. Yang, Y. Xu, B. Zeng, and T. Chilimbi, “Multi-modal alignment using representation codebook,” 2022.
  * [6] J. Li, R. R. Selvaraju, A. D. Gotmare, S. Joty, C. Xiong, and S. Hoi, “Align before fuse: Vision and language representation learning with momentum distillation,” 2021.
  * [7] C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. V. Le, Y. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” 2021.
  * [8] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” 2021.
  * [9] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” 2020.
  * [10] J. Wu, W. Gan, Z. Chen, S. Wan, and P. S. Yu, “Multimodal large language models: A survey,” 2023.
  * [11] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” 2015.
  * [12] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” 2023.
  * [13] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020.
  * [14] H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” 2024.
  * [15] D. Zhu, J. Chen, X. Shen, X. Li, and M. Elhoseiny, “Minigpt-4: Enhancing vision-language understanding with advanced large language models,” 2023.
  * [16] Y. Zou and Q. Xie, “A survey on vqa: Datasets and approaches,” in _2020 2nd International Conference on Information Technology and Computer Application (ITCA)_. IEEE, Dec. 2020. [Online]. Available: http://dx.doi.org/10.1109/ITCA52113.2020.00069
  * [17] Z. Liu, C. Rodriguez-Opazo, D. Teney, and S. Gould, “Image retrieval on real-life images with pre-trained vision-and-language models,” 2021.
  * [18] K. Srinivasan, K. Raman, J. Chen, M. Bendersky, and M. Najork, “Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning,” in _Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval_ , ser. SIGIR ’21. ACM, Jul. 2021. [Online]. Available: http://dx.doi.org/10.1145/3404835.3463257
  * [19] W. Zhu, J. Hessel, A. Awadalla, S. Y. Gadre, J. Dodge, A. Fang, Y. Yu, L. Schmidt, W. Y. Wang, and Y. Choi, “Multimodal c4: An open, billion-scale corpus of images interleaved with text,” 2023.
  * [20] S. Gunasekar, Y. Zhang, J. Aneja, C. C. T. Mendes, A. D. Giorno, S. Gopi, M. Javaheripi, P. Kauffmann, G. de Rosa, O. Saarikivi, A. Salim, S. Shah, H. S. Behl, X. Wang, S. Bubeck, R. Eldan, A. T. Kalai, Y. T. Lee, and Y. Li, “Textbooks are all you need,” 2023.
  * [21] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:160025533
  * [22] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ , 2022, pp. 10 684–10 695.
  * [23] D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach, “Sdxl: Improving latent diffusion models for high-resolution image synthesis,” _arXiv preprint arXiv:2307.01952_ , 2023.
  * [24] J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in _International conference on machine learning_. PMLR, 2022, pp. 12 888–12 900.
  * [25] Deep-Floyd. (n.d.) If. Retrieved June 14, 2024. [Online]. Available: https://github.com/deep-floyd/IF
  * [26] Stability AI, “Stable diffusion,” https://github.com/Stability-AI/stablediffusion, n.d., accessed: 2024-06-14.
  * [27] P. Lu, S. Mishra, T. Xia, L. Qiu, K.-W. Chang, S.-C. Zhu, O. Tafjord, P. Clark, and A. Kalyan, “Learn to explain: Multimodal reasoning via thought chains for science question answering,” 2022.
  * [28] K. Marino, M. Rastegari, A. Farhadi, and R. Mottaghi, “Ok-vqa: A visual question answering benchmark requiring external knowledge,” 2019.
  * [29] D. Schwenk, A. Khandelwal, C. Clark, K. Marino, and R. Mottaghi, “A-okvqa: A benchmark for visual question answering using world knowledge,” 2022.
  * [30] A. Mishra, S. Shekhar, A. K. Singh, and A. Chakraborty, “Ocr-vqa: Visual question answering by reading text in images,” in _ICDAR_ , 2019.
  * [31] O. Sidorov, R. Hu, M. Rohrbach, and A. Singh, “Textcaps: a dataset for image captioning with reading comprehension,” 2020.
  * [32] S. Kazemzadeh, V. Ordonez, M. Matten, and T. Berg, “ReferItGame: Referring to objects in photographs of natural scenes,” in _Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , A. Moschitti, B. Pang, and W. Daelemans, Eds. Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 787–798. [Online]. Available: https://aclanthology.org/D14-1086
  * [33] V. Ordonez, G. Kulkarni, and T. Berg, “Im2text: Describing images using 1 million captioned photographs,” in _Advances in Neural Information Processing Systems_ , J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, Eds., vol. 24. Curran Associates, Inc., 2011.
  * [34] R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, M. S. Bernstein, and F.-F. Li, “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” 2016.
  * [35] S. Changpinyo, P. Sharma, N. Ding, and R. Soricut, “Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,” 2021.
  * [36] OpenAI, “Gpt-4 technical report,” 2024.
  * [37] L. Chen, J. Li, X. Dong, P. Zhang, C. He, J. Wang, F. Zhao, and D. Lin, “Sharegpt4v: Improving large multi-modal models with better captions,” 2023.
  * [38] X. Dai, J. Hou, C.-Y. Ma, S. Tsai, J. Wang, R. Wang, P. Zhang, S. Vandenhende, X. Wang, A. Dubey _et al._ , “Emu: Enhancing image generation models using photogenic needles in a haystack,” _arXiv preprint arXiv:2309.15807_ , 2023.
  * [39] I. Z. Yalniz, H. Jégou, K. Chen, M. Paluri, and D. Mahajan, “Billion-scale semi-supervised learning for image classification,” 2019.
  * [40] C. Zauner, “Implementation and benchmarking of perceptual image hash functions,” 2010. [Online]. Available: https://api.semanticscholar.org/CorpusID:17075066
  * [41] A. Awadalla, I. Gao, J. Gardner, J. Hessel, Y. Hanafy, W. Zhu, K. Marathe, Y. Bitton, S. Gadre, S. Sagawa, J. Jitsev, S. Kornblith, P. W. Koh, G. Ilharco, M. Wortsman, and L. Schmidt, “Openflamingo: An open-source framework for training large autoregressive vision-language models,” 2023.
  * [42] T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, “Microsoft coco: Common objects in context,” 2015.
  * [43] LDNOOBW, “List of dirty, naughty, obscene, and otherwise bad words,” https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words, n.d., accessed: 2024-06-14.
  * [44] P. Schramowski, C. Tauchmann, and K. Kersting, “Can machines help us answering question 16 in datasheets, and in turn reflecting on inappropriate content?” 2022.
  * [45] P. P. Liang, C. Wu, L.-P. Morency, and R. Salakhutdinov, “Towards understanding and mitigating social biases in language models,” 2021.
  * [46] I. O. Gallegos, R. A. Rossi, J. Barrow, M. M. Tanjim, S. Kim, F. Dernoncourt, T. Yu, R. Zhang, and N. K. Ahmed, “Bias and fairness in large language models: A survey,” 2024.
  * [47] ——, “Bias and Fairness in Large Language Models: A Survey,” _Computational Linguistics_ , pp. 1–79, 06 2024. [Online]. Available: https://doi.org/10.1162/coli_a_00524
  * [48] X. Lu, P. West, R. Zellers, R. Le Bras, C. Bhagavatula, and Y. Choi, “NeuroLogic decoding: (un)supervised neural text generation with predicate logic constraints,” in _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_ , K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, and Y. Zhou, Eds. Online: Association for Computational Linguistics, Jun. 2021, pp. 4288–4299. [Online]. Available: https://aclanthology.org/2021.naacl-main.339
  * [49] A. S. Luccioni, C. Akiki, M. Mitchell, and Y. Jernite, “Stable bias: Analyzing societal representations in diffusion models,” 2023.
  * [50] H. Bansal, D. Yin, M. Monajatipoor, and K.-W. Chang, “How well can text-to-image generative models understand ethical natural language interventions?” 2022.
  * [51] R. Naik and B. Nushi, “Social biases through the text-to-image generation lens,” 2023.
  * [52] H. Berg, S. M. Hall, Y. Bhalgat, W. Yang, H. R. Kirk, A. Shtedritski, and M. Bain, “A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning,” 2022.
  * [53] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “Llama: Open and efficient foundation language models,” 2023.