AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering

• URL: http://arxiv.org/abs/2407.19410v1
• Date: Sun, 28 Jul 2024 06:23:06 GMT
• Title: AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering
• Authors: Mahiro Ukai, Shuhei Kurita, Atsushi Hashimoto, Yoshitaka Ushiku, Nakamasa Inoue,
• Abstract summary: We propose AdaCoder, an adaptive prompt compression framework for visual question answering models. AdaCoder operates in two phases: a compression phase and an inference phase. We demonstrate that it reduces token length by 71.1%, while maintaining or even improving the performance of visual question answering.
• Score: 23.169961738978614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual question answering aims to provide responses to natural language questions given visual input. Recently, visual programmatic models (VPMs), which generate executable programs to answer questions through large language models (LLMs), have attracted research interest. However, they often require long input prompts to provide the LLM with sufficient API usage details to generate relevant code. To address this limitation, we propose AdaCoder, an adaptive prompt compression framework for VPMs. AdaCoder operates in two phases: a compression phase and an inference phase. In the compression phase, given a preprompt that describes all API definitions in the Python language with example snippets of code, a set of compressed preprompts is generated, each depending on a specific question type. In the inference phase, given an input question, AdaCoder predicts the question type and chooses the appropriate corresponding compressed preprompt to generate code to answer the question. Notably, AdaCoder employs a single frozen LLM and pre-defined prompts, negating the necessity of additional training and maintaining adaptability across different powerful black-box LLMs such as GPT and Claude. In experiments, we apply AdaCoder to ViperGPT and demonstrate that it reduces token length by 71.1%, while maintaining or even improving the performance of visual question answering.

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