Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts

• URL: http://arxiv.org/abs/2406.16851v2
• Date: Wed, 3 Jul 2024 03:55:59 GMT
• Title: Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts
• Authors: Aditya Sharma, Michael Saxon, William Yang Wang,
• Abstract summary: We present LoCoVQA, a benchmark generator for evaluating long-context extractive reasoning in vision language models (VLMs) LoCoVQA augments test examples for mathematical reasoning, VQA, and character recognition tasks with increasingly long visual contexts. This test assesses how well VLMs can ignore irrelevant information when answering queries.
• Score: 65.04791072532106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present LoCoVQA, a dynamic benchmark generator for evaluating long-context extractive reasoning in vision language models (VLMs). LoCoVQA augments test examples for mathematical reasoning, VQA, and character recognition tasks with increasingly long visual contexts composed of both in-distribution and out-of-distribution distractor images. Across these tasks, a diverse set of VLMs rapidly lose performance as the visual context length grows, often exhibiting a striking logarithmic decay trend. This test assesses how well VLMs can ignore irrelevant information when answering queries -- a task that is quite easy for language models (LMs) in the text domain -- demonstrating that current state-of-the-art VLMs lack this essential capability for many long-context applications.

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