Demonstration of InsightPilot: An LLM-Empowered Automated Data
Exploration System
- URL: http://arxiv.org/abs/2304.00477v2
- Date: Mon, 13 Nov 2023 02:48:47 GMT
- Title: Demonstration of InsightPilot: An LLM-Empowered Automated Data
Exploration System
- Authors: Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, Dongmei Zhang
- Abstract summary: We introduce InsightPilot, an automated data exploration system designed to simplify the data exploration process.
InsightPilot automatically selects appropriate analysis intents, such as understanding, summarizing, and explaining.
In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts.
- Score: 48.62158108517576
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Exploring data is crucial in data analysis, as it helps users understand and
interpret the data more effectively. However, performing effective data
exploration requires in-depth knowledge of the dataset and expertise in data
analysis techniques. Not being familiar with either can create obstacles that
make the process time-consuming and overwhelming for data analysts. To address
this issue, we introduce InsightPilot, an LLM (Large Language Model)-based,
automated data exploration system designed to simplify the data exploration
process. InsightPilot automatically selects appropriate analysis intents, such
as understanding, summarizing, and explaining. Then, these analysis intents are
concretized by issuing corresponding intentional queries (IQueries) to create a
meaningful and coherent exploration sequence. In brief, an IQuery is an
abstraction and automation of data analysis operations, which mimics the
approach of data analysts and simplifies the exploration process for users. By
employing an LLM to iteratively collaborate with a state-of-the-art insight
engine via IQueries, InsightPilot is effective in analyzing real-world
datasets, enabling users to gain valuable insights through natural language
inquiries. We demonstrate the effectiveness of InsightPilot in a case study,
showing how it can help users gain valuable insights from their datasets.
Related papers
- Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs [0.061446808540639365]
This work explores the usage of Knowledge Graphs (KG) as a basic framework for capturing a human-centered manner complex analytics.
The data stored in the generated KG can then be exploited to provide assistance (e.g., recommendations) to the users interacting with these systems.
arXiv Detail & Related papers (2024-11-01T20:45:23Z) - Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data.
We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation.
Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
arXiv Detail & Related papers (2024-10-31T06:55:24Z) - PUB: Plot Understanding Benchmark and Dataset for Evaluating Large Language Models on Synthetic Visual Data Interpretation [2.1184929769291294]
This paper presents a novel synthetic dataset designed to evaluate the proficiency of large language models in interpreting data visualizations.
Our dataset is generated using controlled parameters to ensure comprehensive coverage of potential real-world scenarios.
We employ multimodal text prompts with questions related to visual data in images to benchmark several state-of-the-art models.
arXiv Detail & Related papers (2024-09-04T11:19:17Z) - Data Formulator 2: Iteratively Creating Rich Visualizations with AI [65.48447317310442]
We present Data Formulator 2, an LLM-powered visualization system to address these challenges.
With Data Formulator 2, users describe their visualization intent with blended UI and natural language inputs, and data transformation are delegated to AI.
To support iteration, Data Formulator 2 lets users navigate their iteration history and reuse previous designs towards new ones so that they don't need to start from scratch every time.
arXiv Detail & Related papers (2024-08-28T20:12:17Z) - DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery.
Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering.
Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
arXiv Detail & Related papers (2024-07-01T18:58:22Z) - AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations.
Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
arXiv Detail & Related papers (2024-06-17T04:20:02Z) - DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation [83.30006900263744]
Data analysis is a crucial analytical process to generate in-depth studies and conclusive insights.
We propose to automatically generate high-quality answer annotations leveraging the code-generation capabilities of LLMs.
Our DACO-RL algorithm is evaluated by human annotators to produce more helpful answers than SFT model in 57.72% cases.
arXiv Detail & Related papers (2024-03-04T22:47:58Z) - Lightweight Knowledge Representations for Automating Data Analysis [33.094930396228676]
We take the first steps towards automating a key aspect of the data science pipeline: data analysis.
We present an taxonomy of data analytic operations that scopes analytics across domains and data, as well as a method for codifying domain-specific knowledge that links this taxonomy to actual data.
In this way, we produce information spaces over data that enable complex analyses and search over this data scopes and pave the way for fully automated data analysis.
arXiv Detail & Related papers (2023-10-15T06:44:45Z) - Learn to Explore: on Bootstrapping Interactive Data Exploration with
Meta-learning [8.92180350317399]
We propose a learning-to-explore framework, based on meta-learning, which learns how to learn a classifier with automatically generated meta-tasks.
Our proposal outperforms existing explore-by-example solutions in terms of accuracy and efficiency.
arXiv Detail & Related papers (2022-12-07T03:12:41Z) - Interactive Data Analysis with Next-step Natural Language Query
Recommendation [34.264322423228556]
We develop an NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions.
The system helps users organize query histories and results into a dashboard to communicate the discovered data insights.
arXiv Detail & Related papers (2022-01-13T10:20:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.