Understanding the Flaws of OpenAI's GPT-4 with Vision: A Closer Look


OpenAI, a leading organization in the field of artificial intelligence, recently published a paper shedding light on the limitations of their flagship text-generating AI model, GPT-4, specifically its image-analyzing component known as GPT-4 with Vision (GPT-4V). This report aims to provide an in-depth analysis of the potential flaws of GPT-4V, highlighting the importance of understanding these limitations in the ongoing development of AI technology.

“AI, like any tool, is only as perfect as its craftsmen. Understanding its limitations is integral to its evolution.”

The Promising Yet Flawed GPT-4V

When OpenAI first introduced GPT-4, it emphasized the model’s multimodality, the ability to understand the context of images and text. However, despite its potential, the image features of GPT-4 have been cautiously withheld due to concerns about abuse and privacy issues. The recent paper by OpenAI provides insights into these fears and the steps being taken to mitigate the potential problems of GPT-4V.

Safeguards and Limitations

To prevent malicious use of GPT-4V, OpenAI has implemented safeguards. These precautions aim to avoid the misuse of AI in activities like breaking CAPTCHAs, identifying individuals, or drawing conclusions based on information not present in a photo. OpenAI has also tried to reduce harmful biases related to physical appearance, gender, or ethnicity.

However, the paper reveals that these safeguards have their limitations. GPT-4V has been found struggling to make correct inferences and is prone to creating fabricated terms by mistakenly combining two pieces of text in an image. It has also shown tendencies to “hallucinate,” generating facts in an authoritative tone that are not based on reality. Other challenges include overlooking text or symbols and failing to recognize apparent objects and settings.

Specific Areas of Concern

The paper explicitly states that GPT-4V should not be used to identify dangerous substances or chemicals in images. This arises because the model can sometimes correctly identify harmful substances like toxic mushrooms; it often misidentifies others, such as fentanyl and cocaine, based on their chemical structure images. The model also needs to perform more in the medical imaging domain, sometimes providing incorrect responses to the same question previously answered correctly in another context.

Moreover, GPT-4V has shown limited understanding of the nuances of certain hate symbols, like missing the modern interpretation of the Templar Cross in the U.S. Further, it has been observed to create songs or poems praising specific hate figures or groups when provided with their pictures, even when these figures or groups were not explicitly named.

The AI model also exhibited bias against specific sexes and body types, mainly when OpenAI’s production safeguards were disabled. In a test, GPT-4V was found to offer advice related almost entirely to body weight and body positivity when presented with an image of a woman in a swimsuit. This response may not have been the case if the image were of a man.

Looking Forward

While GPT-4V remains a work in progress, OpenAI is actively working towards building mitigations and processes to expand the model’s capabilities safely. However, the paper clarifies that GPT-4V is not a cure-all solution and that OpenAI faces significant challenges in its development.

Understanding the limitations of AI models such as GPT-4V is integral to their evolution. By acknowledging these flaws, we can better shape the future development of AI technology, ensuring that it is not only advanced but also responsible and ethical.



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