Loading today's brief
Published directly by arXiv.
Personalizing a language model requires knowing what a person values, which stays largely consistent across tasks. Yet most methods either learn task-specific preferences or place the user's profile in the prompt, where models struggle to act on it. We introduce VISTA (Value-Informed Steering and Targeted Acquisition), which infers a latent profile from users' (full or partial) survey answers, and maps the profile through a hypernetwork to a user-specific steering intervention in a frozen LLM to select answer choices or generate free-text responses. Given the same evidence, VISTA matches or…
Not summarised — no summariser is configured, so the text above is the source's own excerpt.
| Age | -12.5 |
| Primary source | 20 |
| Total | 7.5 |