  Designing effective data manipulation methods is a long standing problem in
data lakes. Traditional methods, which rely on rules or machine learning
models, require extensive human efforts on training data collection and tuning
models. Recent methods apply Large Language Models (LLMs) to resolve multiple
data manipulation tasks. They exhibit bright benefits in terms of performance
but still require customized designs to fit each specific task. This is very
costly and can not catch up with the requirements of big data lake platforms.
In this paper, inspired by the cross-task generality of LLMs on NLP tasks, we
pave the first step to design an automatic and general solution to tackle with
data manipulation tasks. We propose UniDM, a unified framework which
establishes a new paradigm to process data manipulation tasks using LLMs. UniDM
formalizes a number of data manipulation tasks in a unified form and abstracts
three main general steps to solve each task. We develop an automatic context
retrieval to allow the LLMs to retrieve data from data lakes, potentially
containing evidence and factual information. For each step, we design effective
prompts to guide LLMs to produce high quality results. By our comprehensive
evaluation on a variety of benchmarks, our UniDM exhibits great generality and
state-of-the-art performance on a wide variety of data manipulation tasks.
