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任务的开源大语言模型通用求解器。Jellyfish基于Llama 2 13B模型构建，通过包含错误检测、数据插补、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，并展现出对其他任务的泛化能力。该模型以130亿参数规模可在本地单块低成本GPU上运行，既保障数据安全又支持后续微调。其对自然语言的理解能力使用户能够为数据预处理任务手动构建指令。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，并可在推理阶段整合可选的知识注入。其独特设计在于配备了解释器模块，能够阐明输出决策的生成依据。为构建Jellyfish，我们研发了系列预微调与数据预处理微调技术。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提升数据预处理性能。我们在多个真实数据集上的评估表明，Jellyfish与现有最优方法相比具有竞争力，且对未见任务展现出强泛化能力。该模型性能可媲美GPT系列模型，其解释器相比GPT-3.5提供了更强的推理能力。此外，评估结果验证了Jellyfish构建中所用技术的有效性。模型已发布在Hugging Face平台：https://huggingface.co/NECOUDBFM/Jellyfish