The study investigates how different prompt components affect language model responses in psychometric tests. By crossing five distinct baseline personas with five variants of each prompt element—persona wording, task instruction, item wording, and option symbol—the authors measure response shifts using the 1‑Wasserstein distance. Their analysis of 13 small open‑weight language models on the Big Five Inventory and Short Dark Triad reveals that task instruction and option symbol changes often cause more variation than paraphrasing the persona or item, with prompt artifacts explaining over 50% of the variation for many items.
arXiv:2606. 03357v1 Announce Type: cross Abstract: When prompting SLMs for psychometric assessments, researchers assume the outputs reflect semantic reasoning.
arXiv:2608.22438v1 Announce Type: new Abstract: Persona-conditioned large language models (LLMs) are increasingly used to simulate survey responses across diverse domains. However, apparent response...
arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.
arXiv:2606. 05486v1 Announce Type: cross Abstract: Prompt ambiguity is a common source of failure in large language models, but is difficult to localize because it is a latent property of the prompt, while existing attribution methods are designed to explain observable outputs such as logits or generated tokens.
arXiv:2609.02526v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona promptin...
The paper investigates how the language of prompts and responses affects large language model (LLM) outputs. Using five models and 68 non‑translation questions, the authors compare English‑to‑English, English‑to‑Norwegian, Norwegian‑to‑Norwegian, and Norwegian‑to‑English conditions, yielding 1,348 responses after filtering. They find that prompt language strongly influences response length—Norwegian prompts shorten English outputs by ~37 % and English prompts shorten Norwegian outputs by ~41 %—while semantic similarity remains high across conditions.
arXiv:2608. 10385v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as relevance assessors in information retrieval (IR) evaluation, raising questions about how assessor framing affects judgment reliability and downstream system comparison.
arXiv:2510. 22170v3 Announce Type: replace Abstract: Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation.
The paper proposes a model-based evaluation framework that merges multidimensional item response theory (IRT) with question context embeddings to predict large language model (LLM) performance on unseen questions. By representing LLMs with latent capability profiles and incorporating question content to inform item characteristics, the approach improves prediction accuracy over model-free baselines in within-scenario settings and offers a richer description of capability variation than unidimensional models. However, the study also finds that this generalizability does not reliably extend to cross-scenario shifts, indicating a key limitation for broader application.
arXiv:2604. 22027v2 Announce Type: replace-cross Abstract: One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the question is posed.
arXiv:2607. 13162v1 Announce Type: cross Abstract: What a language model will and will not do is largely set during post-training, but which behaviors it expresses, hides, or resists is not revealed by prompting alone.
arXiv:2604. 07102v2 Announce Type: replace-cross Abstract: Activation-based steering enables inference-time personalization of large language models, but its effects in educational applications are not well understood.