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arXiv:2608.29798v1 Announce Type: cross Abstract: The same Persona behavior can be beneficial in one context but harmful in another, causing static Persona elicitation to perform inconsistently acros...
arXiv:2609.39882v1 Announce Type: new Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-...
arXiv:2608. 13482v1 Announce Type: cross Abstract: As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical.
arXiv:2606. 18263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks.
arXiv:2607. 26473v1 Announce Type: new Abstract: Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings.
PersonaForge is a user‑simulation framework that generates realistic multi‑turn interactions between users and agentic systems, addressing the gap that most training data assumes single‑turn queries. It uses a four‑dimensional persona space, SOUL‑driven behavioral control calibrated to real‑user statistics, and Reverse Deep Construction from authentic seed queries to create a 6.3K‑record training set and a 138‑task benchmark called PersonaForge‑Bench across 20 professional domains. Experiments with Qwen3.5‑27B show that training with PersonaForge improves composite scores by 4.1%, especially in Task Completion (+6.0%) and Response Quality (+6.8%), while also reducing turns and tool calls, indicating more efficient interactions.