VLM-IMI adapts VLMs with iterative and manual instructions plus a learnable fusion module to guide diffusion-based generative low-light image enhancement, outperforming prior methods in perceptual quality.
Black-box prompt optimization: Aligning large language models without model training
3 Pith papers cite this work. Polarity classification is still indexing.
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SGT trains a lightweight model to generate task-specific supplemental text that improves performance of a larger frozen LLM on agentic tasks without modifying the large model.
Empirical tests on three LLMs show prompt semantics and task keywords drive inference energy costs more than length, with varying patterns by task.
citing papers explorer
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Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement
VLM-IMI adapts VLMs with iterative and manual instructions plus a learnable fusion module to guide diffusion-based generative low-light image enhancement, outperforming prior methods in perceptual quality.
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Supplement Generation Training for Enhancing Agentic Task Performance
SGT trains a lightweight model to generate task-specific supplemental text that improves performance of a larger frozen LLM on agentic tasks without modifying the large model.
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Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference
Empirical tests on three LLMs show prompt semantics and task keywords drive inference energy costs more than length, with varying patterns by task.