Leveraging Foundation Models for Unified Data Discovery and Exploration

Centrala begrepp

Foundation models show promising performance on a range of diverse tasks unrelated to their training, making them highly applicable to the data discovery and data exploration domain. When carefully used, they outperform task-specific models and even human experts on three representative tasks: table-class detection, column-type annotation, and join-column prediction.

Sammanfattning

The paper explores the use of foundation models, which are large language models (LLMs) that can generalize to diverse domain-specific tasks, for data discovery and exploration tasks. It proposes a novel system called Chorus that leverages foundation models to perform three representative tasks:

Table-class detection: Assigning an appropriate DBPedia.org ontology class to each table in a data collection.
Column-type annotation: Mapping the columns of each table to the corresponding types in a reference ontology.
Join-column prediction: Suggesting the columns to use for joining two tables, based on a history of past user actions.

Chorus has a unified architecture that allows information flow between tasks. It generates prompts by combining context, demonstrations, data samples, metadata, task-specific knowledge, and prefixes, which are then fed to the foundation model. Chorus also includes post-processing steps to check the feasibility of the outputs and mitigate errors through a novel technique called "anchoring".
The paper evaluates Chorus on benchmark datasets for the three tasks and compares its performance to state-of-the-art baselines. Chorus outperforms the baselines on all three tasks, often surpassing human-expert performance. The paper also investigates the fundamental characteristics of this approach, including its generalizability to different foundation models and the impact of non-determinism on the outputs.
Overall, the results suggest that foundation models hold promise as a core component of next-generation data discovery systems, enabling a unified approach to diverse data management tasks.

Statistik

"We apply foundation models to data discovery and exploration tasks."
"Foundation models are large language models (llms) that show promising performance on a range of diverse tasks unrelated to their training."
"On all three tasks, we show that a foundation-model-based approach outperforms the task-specific models and so the state of the art. Further, our approach often surpasses human-expert task performance."

Citat

"Foundation models are large language models (llms) that show promising performance on a range of diverse tasks unrelated to their training."
"We show that these models are highly applicable to the data discovery and data exploration domain."
"When carefully used, they have superior capability on three representative tasks: table-class detection, column-type annotation and join-column prediction."

Viktiga insikter från

CHORUS

by Moe Kayali,A... på arxiv.org 04-09-2024

https://arxiv.org/pdf/2306.09610.pdf

Djupare frågor

How can foundation models be further extended to support other data management tasks beyond the three explored in this paper?

Foundation models can be extended to support a wide range of data management tasks beyond the ones explored in this paper. One approach is to apply foundation models to tasks such as data profiling, data cleaning, entity resolution, outlier detection, and data provenance tracking. By providing appropriate prompts and context, foundation models can be leveraged to automate these tasks efficiently. Additionally, foundation models can be used for tasks like schema auto-completion, attribute synonym finding, and join-graph traversal, enabling seamless data exploration and analysis.

What are the potential limitations or risks of relying on foundation models for data discovery, and how can they be mitigated?

While foundation models offer significant advantages in data discovery tasks, there are potential limitations and risks that need to be addressed. One major risk is the potential for spurious generation or hallucination, where the model generates incorrect or misleading outputs. This can be mitigated by implementing robust post-processing checks, such as feasibility checks and anchoring, to correct errors and prevent their propagation. Additionally, ensuring the quality and diversity of training data, as well as monitoring model performance and biases, can help mitigate risks associated with foundation models.

How might the insights from this work on applying foundation models to data management tasks inform the broader development and deployment of foundation models in other domains?

The insights gained from applying foundation models to data management tasks can inform the broader development and deployment of foundation models in other domains. By showcasing the capabilities and limitations of foundation models in data discovery tasks, this work provides valuable guidance for leveraging foundation models in diverse domains such as natural language processing, image recognition, and healthcare. The methodologies and best practices developed for data management tasks can be adapted and applied to other domains to enhance the performance and reliability of foundation models across various applications.