University of Amsterdam at the CLEF 2025 Eloquent Track:Evaluating the Influence of Stylistic Prompt Variations on Semantic Interpretation
Bibliographic record
Abstract
This paper reports on the University of Amsterdam’s participation in the CLEF 2025 Eloquent Track’s Robustness and Consistency Task. Our overall goal is to evaluate the influence of stylistic prompt variations on semantic interpretation. Our specific focus is to investigate how variations in prompt tone, structure, and persona affect the consistency and robustness of responses generated by large language models (LLMs). We approach this through two complementary methods. First, we use a model-as-judge setup to quantify semantic consistency: each stylistic variant prompt is compared to its original base prompt using GPT-4.1 to rate the similarity of the generated responses on a 0–5 scale. Second, we conduct an inductive qualitative analysis on a selected prompt to closely examine how different stylistic framings influence content shifts in model outputs. Our results suggest that prompt reformulations can lead to variations in output, informational content, and tone.
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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 | 0.016 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".