Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learned

• URL: http://arxiv.org/abs/2407.21040v1
• Date: Sun, 21 Jul 2024 08:58:18 GMT
• Title: Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learned
• Authors: Yuan Liao, Jiang Bian, Yuhui Yun, Shuo Wang, Yubo Zhang, Jiaming Chu, Tao Wang, Kewei Li, Yuchen Li, Xuhong Li, Shilei Ji, Haoyi Xiong,
• Abstract summary: This study introduces SageCopilot, an advanced, industry-grade system system that automates the data science pipeline. SageCopilot incorporates an online component refining users' inputs into executable scripts through In-Context Learning (ICL) and running the scripts for results reporting & visualization. A list of trending strategies such as Chain-of-Thought and prompt-tuning have been used to augment SageCopilot for enhanced performance.
• Score: 29.847460840760334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the field of NL2SQL has made significant advancements in translating natural language instructions into executable SQL scripts for data querying and processing, achieving full automation within the broader data science pipeline - encompassing data querying, analysis, visualization, and reporting - remains a complex challenge. This study introduces SageCopilot, an advanced, industry-grade system system that automates the data science pipeline by integrating Large Language Models (LLMs), Autonomous Agents (AutoAgents), and Language User Interfaces (LUIs). Specifically, SageCopilot incorporates a two-phase design: an online component refining users' inputs into executable scripts through In-Context Learning (ICL) and running the scripts for results reporting & visualization, and an offline preparing demonstrations requested by ICL in the online phase. A list of trending strategies such as Chain-of-Thought and prompt-tuning have been used to augment SageCopilot for enhanced performance. Through rigorous testing and comparative analysis against prompt-based solutions, SageCopilot has been empirically validated to achieve superior end-to-end performance in generating or executing scripts and offering results with visualization, backed by real-world datasets. Our in-depth ablation studies highlight the individual contributions of various components and strategies used by SageCopilot to the end-to-end correctness for data sciences.

Related papers

• EICopilot: Search and Explore Enterprise Information over Large-scale Knowledge Graphs with LLM-driven Agents [16.65035686422735]
  The paper introduces EICopilot, a novel agent-based solution enhancing search and exploration of enterprise registration data within online knowledge graphs. The solution automatically generates and executes Gremlin scripts, providing efficient summaries of complex enterprise relationships. Empirical evaluations demonstrate the superior performance of EICopilot, including speed and accuracy, over baseline methods.
  arXiv  Detail & Related papers  (2025-01-23T15:22:25Z)
• ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis [80.34000499166648]
  We propose a Graph-based Sampling strategy to sample more relevant tool combinations, and a Planned-generation strategy to create plans that guide the synthesis of coherent dialogues. We apply SFT on LLaMA-3.1-8B using 8,000 synthetic dialogues generated with ToolFlow. Results show that the model achieves tool-calling performance comparable to or even surpassing GPT-4, while maintaining strong general capabilities.
  arXiv  Detail & Related papers  (2024-10-24T05:45:04Z)
• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL [0.0]
  We utilize OpenAI's GPT-4 model within a retrieval-augmented generation (RAG) framework. Business context document is enriched with a business context document, to transform NLQs into Structured Query Language queries. Performance achieved a maximum of 85% when high complexity queries are excluded.
  arXiv  Detail & Related papers  (2024-06-15T17:07:31Z)
• Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine [0.9576975587953563]
  Pre-trained language models (PLM) based on transformer neural networks offer great opportunities to improve automatic content analysis in communication science. Three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine.
  arXiv  Detail & Related papers  (2023-12-28T11:39:08Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• ChatGPT as your Personal Data Scientist [0.9689893038619583]
  This paper introduces a ChatGPT-based conversational data-science framework to act as a "personal data scientist" Our model pivots around four dialogue states: Data visualization, Task Formulation, Prediction Engineering, and Result Summary and Recommendation. In summary, we developed an end-to-end system that not only proves the viability of the novel concept of conversational data science but also underscores the potency of LLMs in solving complex tasks.
  arXiv  Detail & Related papers  (2023-05-23T04:00:16Z)
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting [143.4162028260874]
  We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
  arXiv  Detail & Related papers  (2022-10-04T18:32:14Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

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.