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the Ada coder framework is a novel adaptive prompt compression framework for visual programmatic models vpms that generates executable programs to answer questions through large language models llms it operates in two phases compression and inference in the compression phase a set of compressed pre-prom is generated for specific question types the inference phase adaptively selects a compressed prompt classifies the question type and generates a python PR to answer the input question Ada coder addresses the limitation of vpms by reducing the input length and computational load while maintaining or improving question answering performance the framework employs a single Frozen llm and predefined prompts negating the necessity of additional training it outperforms state-of-the-art models like Viper GPT and llm lingua in evaluations the paper also reviews related work in visual question answer and discusses the importance of interpretability and generalizability in vqa models Ada coder is
