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:
this https URL .

在本文中，我们介绍了 Jellyfish，一种基于开源 LLM 的通用数据隐私求解器，它能够在解决各种数据隐私任务时展现出通用性，并且具备理解自然语言的能力。Jellyfish 不仅能够在本地、单个低成本 GPU 上运行，并确保数据安全和进一步调试。我们构建 Jellyfish 所采用的技术包括预调优和数据隐私调优技术，同时拥有解释器，对其输出结果进行解释，并通过知识注入来提高数据隐私的性能。我们使用各种真实数据集对 Jellyfish 进行评估，结果显示它与最先进的方法相比具有竞争力且具有很强的泛化能力。Jellyfish 的性能与 GPT 系列模型相当，并且其解释器相较于 GPT-3.5 具有增强的推理能力。同时，我们的评估结果也突显了构建 Jellyfish 所采用的技术的有效性。