A Surprising Failure? Multimodal LLMs and the NLVR Challenge
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914693430378496 |
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| author | Wu, Anne Brantley, Kianté Artzi, Yoav |
| author_facet | Wu, Anne Brantley, Kianté Artzi, Yoav |
| contents | This study evaluates three state-of-the-art MLLMs -- GPT-4V, Gemini Pro, and the open-source model IDEFICS -- on the compositional natural language vision reasoning task NLVR. Given a human-written sentence paired with a synthetic image, this task requires the model to determine the truth value of the sentence with respect to the image. Despite the strong performance demonstrated by these models, we observe they perform poorly on NLVR, which was constructed to require compositional and spatial reasoning, and to be robust for semantic and systematic biases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Surprising Failure? Multimodal LLMs and the NLVR Challenge Wu, Anne Brantley, Kianté Artzi, Yoav Artificial Intelligence Computation and Language Machine Learning This study evaluates three state-of-the-art MLLMs -- GPT-4V, Gemini Pro, and the open-source model IDEFICS -- on the compositional natural language vision reasoning task NLVR. Given a human-written sentence paired with a synthetic image, this task requires the model to determine the truth value of the sentence with respect to the image. Despite the strong performance demonstrated by these models, we observe they perform poorly on NLVR, which was constructed to require compositional and spatial reasoning, and to be robust for semantic and systematic biases. |
| title | A Surprising Failure? Multimodal LLMs and the NLVR Challenge |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.17793 |