Supporting Sensemaking of Large Language Model Outputs at Scale-Reference-Cited by-同舟云学术

Supporting Sensemaking of Large Language Model Outputs at Scale

Published:2024-05-11 Issue: Volume:2 Page:1-21
ISSN:
Container-title:Proceedings of the CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Gero Katy Ilonka¹^ORCID,Swoopes Chelse¹^ORCID,Gu Ziwei²^ORCID,Kummerfeld Jonathan K.³^ORCID,Glassman Elena L.⁴^ORCID

Affiliation:

1. Department of Computer Science, Harvard University, United States

2. Harvard University, United States

3. School of Computer Science, The University of Sydney, Australia

4. SEAS, Harvard University, United States

Funder

NSF (National Science Foundation)

Google

Amazon Research Awards

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613904.3642139

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3. Ebtesam Almazrouei Hamza Alobeidli Abdulaziz Alshamsi Alessandro Cappelli Ruxandra Cojocaru Mérouane Debbah Étienne Goffinet Daniel Hesslow Julien Launay Quentin Malartic Daniele Mazzotta Badreddine Noune Baptiste Pannier and Guilherme Penedo. 2023. The Falcon Series of Open Language Models. (2023). arxiv:2311.16867 [cs.CL]

4. Ian Arawjo Chelse Swoopes Priyan Vaithilingam Martin Wattenberg and Elena Glassman. 2023. ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing. arxiv:2309.09128 [cs.HC]

5. Ian Arawjo Priyan Vaithilingam Chelse Swoopes Martin Wattenberg and Elena Glassman. 2023. ChainForge. https://www.chainforge.ai/. Accessed: 2023-07-21.

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