In this paper, we present Jellyfish, an open-source LLM as a universal task solver for DP. Built on the Llama 2 13B model, Jellyfish is instruction-tuned with the datasets of several typical DP tasks including error detection, data imputation, schema matching, and entity matching, and delivers generalizability to other tasks. Remarkably, Jellyfish can operate on a local, single, and low-priced GPU with its 13 billion parameters, ensuring data security and enabling further tuning. Its proficiency in understanding natural language allows users to manually craft instructions for DP tasks. Unlike many existing methods that heavily rely on prior knowledge, Jellyfish acquires domain knowledge during its tuning process and integrates optional knowledge injection during inference. A distinctive feature of Jellyfish is its interpreter, which elucidates its output decisions. To construct Jellyfish, we develop a series of pre-tuning and DP-tuning techniques. Jellyfish is equipped with an instance serializer, which automatically translates raw data into model prompts, and a knowledge injector, which optionally introduces task- and dataset-specific knowledge to enhance DP performance. Our evaluation of Jellyfish, using a range of real datasets, shows its competitiveness compared to state-of-the-art methods and its strong generalizability to unseen tasks. Jellyfish's performance rivals that of GPT series models, and its interpreter offers enhanced reasoning capabilities compared to GPT-3.5. Furthermore, our evaluation highlights the effectiveness of the techniques employed in constructing Jellyfish. Our model is available at Hugging Face: https://huggingface.co/NECOUDBFM/Jellyfish .

翻译：本文提出Jellyfish，一种面向数据预处理的开源通用任务解决大语言模型。该模型基于Llama 2 13B构建，通过错误检测、数据填补、模式匹配与实体匹配等典型数据预处理任务的数据集进行指令微调，并具备向其他任务泛化的能力。值得注意的是，Jellyfish凭借其130亿参数可在本地单块低成本GPU上运行，保障数据安全并支持进一步微调。其对自然语言的理解能力允许用户手动构建数据预处理任务指令。与许多依赖先验知识的现有方法不同，Jellyfish在微调过程中获取领域知识，并在推理阶段集成可选的知识注入。其显著特征在于配备了输出决策解析器。为构建Jellyfish，我们开发了一系列预微调与数据预处理微调技术。该模型配备实例序列化器，可自动将原始数据转换为模型提示；同时集成知识注入器，可选择性引入任务与数据集特定知识以增强数据预处理性能。基于多个真实数据集的评估表明，Jellyfish在性能上可与最先进方法抗衡，并展现出对未知任务的强大泛化能力。其性能可与GPT系列模型相匹敌，且其解析器相比GPT-3.5具备更强的推理能力。此外，评估结果验证了模型构建中所用技术的有效性。模型已发布于Hugging Face：https://huggingface.co/NECOUDBFM/Jellyfish。