Papers with development cost
Multi-Programming Language Sandbox for LLMs (2025.acl-demo)
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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang
| Challenge: | MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). |
| Approach: | They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models. |
| Outcome: | The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs). |
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments (D19-3)
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| Challenge: | TEASPN is an open-source protocol for integrated writing assistance environments . authors propose that developers and researchers can integrate the latest developments in natural language processing with low cost. |
| Approach: | They propose a protocol and framework for integrating writing aids with writing software. |
| Outcome: | The proposed protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. |
WordKit: a Python Package for Orthographic and Phonological Featurization (L18-1)
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| Challenge: | wordkit is a python package that allows users to switch between feature sets and featurizers with a uniform API . wordkit integrates orthographic and phonological featurizers in a single package . |
| Approach: | They present a python package which allows users to switch between feature sets and featurizers with a uniform API. |
| Outcome: | The proposed package is compatible with scikit-learn and extensible . it allows users to switch between feature sets and featurizers with a uniform API . |