The paper discusses the cost associated with querying large language models (LLMs) and proposes FrugalGPT, a framework that uses LLM APIs to process natural language queries within a budget constraint. The framework uses prompt adaptation, LLM approximation, and LLM cascade to reduce the inference
The paper discusses the use of prompt engineering to leverage pre-trained language models for business process management (BPM) tasks. It identifies the potentials and challenges of prompt engineering for BPM research.
The paper presents the first attempt to use GPT-4 to generate instruction-following data for Large Language Models (LLMs) finetuning. The 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to the instruction-following
The paper proposes Automatic Prompt Engineer (APE), an algorithm that generates and selects natural language instructions for large language models (LLMs) to improve task performance. APE treats the instruction as a program and optimizes it by searching over a pool of instruction candidates propose
The paper proposes a novel communicative agent framework named role-playing to facilitate autonomous cooperation among communicative agents and provide insight into their “cognitive” processes. The approach involves using inception prompting to guide chat agents toward task completion while maintai