Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.516 | 0.899 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.031 | 0.016 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".