ChatBS: An Exploratory Sandbox for Bridging Large Language Models with the Open Web
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Authors
Erickson, John S.
Santos, Henrique
McCusker, Jamie
McGuinness, Deborah L.
Hendler, James A.
Issue Date
2024-11-11
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Article
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Abstract
The recent widespread public availability of generative large language models (LLMs) has drawn much attention from the academic community to run experiments in order to learn more about their strengths and drawbacks. From prompt engineering and fine-tuning to fact-checking and task-solving, researchers have pursued several approaches to try to take advantage of these tools. As some of the most powerful LLMs are ``closed'' and only accessible through web APIs with prior authorization, combining LLMs with the open web is still a challenge. In this evolving landscape, tools that can facilitate the exploration of the capabilities and limitations of LLMs are desirable, especially when connecting with traditional web features such as search and structured data. This article presents ChatBS, a web-based exploratory sandbox for LLMs, working as a front-end for prompting LLMs with user inputs. It provides features such as entity resolution from open knowledge graphs, web search using LLM outputs, as well as popular prompting techniques (e.g. multiple submissions, ``step-by-step''). ChatBS has been extensively used in Rensselaer Polytechnic Institute's Data INCITE courses and research, serving as key tool for utilizing LLMs outputs at scale in these contexts.
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Full Citation
John S. Erickson, , Henrique Santos, Jamie P. McCusker, Sola Shirai, Deborah L. McGuinness, James A. Hendler. "ChatBS: An Exploratory Sandbox for Bridging Large Language Models with the Open Web." Proceedings of the ISWC 2024 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice. CEUR, 2024.
Publisher
CEUR
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ISSN
1613-0073