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Yihuai Lan

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2 papers

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IJCAI Conference 2025 Conference Paper

PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models

Zheng Zhang
Jinyi Li
Yihuai Lan
Xiang Wang
Hao Wang

Prompt engineering enables Large Language Models (LLMs) to perform a variety of tasks. However, lengthy prompts significantly increase computational complexity and economic costs. To address this issue, prompt compression reduces prompt length while maintaining LLM response quality. To support rapid implementation and standardization, we present the Prompt Compression Toolkit (PCToolkit), a unified plug-and-play framework for LLM prompt compression. PCToolkit integrates state-of-the-art compression algorithms, benchmark datasets, and evaluation metrics, enabling systematic performance analysis. Its modular architecture simplifies customization, offering portable interfaces for seamless incorporation of new datasets, metrics, and compression methods. Our code is available at https: //github. com/3DAgentWorld/Toolkit-for-Prompt-Compression. Our demo is at https: //huggingface. co/spaces/CjangCjengh/Prompt-Compression-Toolbox.

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AAAI Conference 2022 System Paper

MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers

Yihuai Lan
Lei Wang
Qiyuan Zhang
Yunshi Lan
Bing Tian Dai
Yan Wang
Dongxiang Zhang
Ee-Peng Lim

While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learningbased MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these MWP datasets are popular datasets that are commonly used as benchmarks in existing work. Our toolkit is featured with highly modularized and reusable components, which can help researchers quickly get started and develop their own models. We have released the code and documentation of MWPToolkit in https: //github. com/LYH-YF/MWPToolkit.

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