# Are Large Language Models the New Interface for Data Pipelines?

arXiv: https://arxiv.org/abs/2406.06596 · HTML: https://mlsanity.com/paper/2406.06596/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning](https://mlsanity.com/paper/2308.12032/) (2023)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [AutoML for Deep Recommender Systems: A Survey](https://mlsanity.com/paper/2203.13922/) (2022)
- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [The Future is Big Graphs! A Community View on Graph Processing Systems](https://mlsanity.com/paper/2012.06171/) (2020)
- [GPT in Data Science: A Practical Exploration of Model Selection](https://mlsanity.com/paper/2311.11516/) (2023)

## Built on by (most cited first, 13 of 13)

- [AraFinNLP 2024: The First Arabic Financial NLP Shared Task](https://mlsanity.com/paper/2407.09818/) (2024)
- [AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data](https://mlsanity.com/paper/2410.11531/) (2024)
- [LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration](https://mlsanity.com/paper/2411.05844/) (2024)
- [Event-Arguments Extraction Corpus and Modeling using BERT for Arabic](https://mlsanity.com/paper/2407.21153/) (2024)
- [Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition](https://mlsanity.com/paper/2506.12615/) (2025)
- [ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task](https://mlsanity.com/paper/2407.20663/) (2024)
- [Automated Archival Descriptions with Federated Intelligence of LLMs](https://mlsanity.com/paper/2504.05711/) (2025)
- [LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition](https://mlsanity.com/paper/2512.22385/) (2025)
- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)
- [Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes](https://mlsanity.com/paper/2502.06918/) (2025)
- [Agentic Control Center for Data Product Optimization](https://mlsanity.com/paper/2603.10133/) (2026)
- [Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving](https://mlsanity.com/paper/2607.05399/) (2026)
- [DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis](https://mlsanity.com/paper/2610.02679/) (2026)

## Abstract (arXiv)

A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency and coherence, making them valuable for a wide range of data-related tasks fashioned as pipelines. The capabilities of LLMs in natural language understanding and generation, combined with their scalability, versatility, and state-of-the-art performance, enable innovative applications across various AI-related fields, including eXplainable Artificial Intelligence (XAI), Automated Machine Learning (AutoML), and Knowledge Graphs (KG). Furthermore, we believe these models can extract valuable insights and make data-driven decisions at scale, a practice commonly referred to as Big Data Analytics (BDA). In this position paper, we provide some discussions in the direction of unlocking synergies among these technologies, which can lead to more powerful and intelligent AI solutions, driving improvements in data pipelines across a wide range of applications and domains integrating humans, computers, and knowledge.