Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks

• URL: http://arxiv.org/abs/2305.13782v1
• Date: Tue, 23 May 2023 07:50:36 GMT
• Title: Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks
• Authors: Sherzod Hakimov, David Schlangen
• Abstract summary: Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models. We make visual information accessible to the language model using separate verbalisation models.
• Score: 17.97052348690598
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. While being actively researched, multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models. In this work, we ask whether language-only models can be utilised for tasks that require visual input -- but also, as we argue, often require a strong reasoning component. Similar to some recent related work, we make visual information accessible to the language model using separate verbalisation models. Specifically, we investigate the performance of open-source, open-access language models against GPT-3 on five vision-language tasks when given textually-encoded visual information. Our results suggest that language models are effective for solving vision-language tasks even with limited samples. This approach also enhances the interpretability of a model's output by providing a means of tracing the output back through the verbalised image content.

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