We Don't Know How to Assess LLM Contributions in VIS/HCI

Submissions of original research that use Large Language Models (LLMs) or that study their behavior, suddenly account for a sizable portion of works submitted and accepted to visualization (VIS) conferences and similar venues in human-computer interaction (HCI). In this brief position paper, I argue that reviewers are relatively unprepared to evaluate these submissions effectively. To support this conjecture I reflect on my experience serving on four program committees for VIS and HCI conferences over the past year. I will describe common reviewer critiques that I observed and highlight how these critiques influence the review process. I also raise some concerns about these critiques that could limit applied LLM research to all but the best-resourced labs. While I conclude with suggestions for evaluating research contributions that incorporate LLMs, the ultimate goal of this position paper is to simulate a discussion on the review process and its challenges.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/beliv64461.2024.00018
Citations
3
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
6.69
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article

We Don't Know How to Assess LLM Contributions in VIS/HCI

Anamaria Crisan
3 citations
Artificial Intelligence in Law
6.69
article

We Don't Know How to Assess LLM Contributions in VIS/HCI

Anamaria Crisan
article en
3 citations

Abstract

Submissions of original research that use Large Language Models (LLMs) or that study their behavior, suddenly account for a sizable portion of works submitted and accepted to visualization (VIS) conferences and similar venues in human-computer interaction (HCI). In this brief position paper, I argue that reviewers are relatively unprepared to evaluate these submissions effectively. To support this conjecture I reflect on my experience serving on four program committees for VIS and HCI conferences over the past year. I will describe common reviewer critiques that I observed and highlight how these critiques influence the review process. I also raise some concerns about these critiques that could limit applied LLM research to all but the best-resourced labs. While I conclude with suggestions for evaluating research contributions that incorporate LLMs, the ultimate goal of this position paper is to simulate a discussion on the review process and its challenges.

University of Waterloo (CA)
Openalex Percentile: Top 3%
Artificial Intelligence in Law
6.69
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