# DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

arXiv: https://arxiv.org/abs/2305.18358 · HTML: https://mlsanity.com/paper/2305.18358/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 26 May 2023.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 8 of 8)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [A Financial Service Chatbot based on Deep Bidirectional Transformers](https://mlsanity.com/paper/2003.04987/) (2020)
- [Talking datasets: Understanding data sensemaking behaviours](https://mlsanity.com/paper/1911.09041/) (2019)
- [Lost or found? Discovering data needed for research](https://mlsanity.com/paper/1909.00464/) (2019)
- [Librarian-in-the-Loop: A Natural Language Processing Paradigm for Detecting Informal Mentions of Research Data in Academic Literature](https://mlsanity.com/paper/2203.05112/) (2022)
- [Subdivisions and Crossroads: Identifying Hidden Community Structures in a Data Archive's Citation Network](https://mlsanity.com/paper/2205.08395/) (2022)

## Built on by (most cited first, 2 of 2)

- [NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes](https://mlsanity.com/paper/2312.14890/) (2023)
- [Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities](https://mlsanity.com/paper/2406.02787/) (2024)

## Abstract (arXiv)

Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter-university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine-readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot-based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.