The paper investigates whether fine‑tuning large language models (LLMs) on value survey responses can shift their value‑conditioned outputs and downstream behavior. Researchers first estimate baseline value profiles for several open‑source LLMs using the ten Schwartz values, then fine‑tune the models on survey‑style supervision that selectively alters target value ratings while preserving non‑target values. The resulting shifts are evaluated on held‑out survey questions and on out‑of‑domain behavioral proxies, such as a contextual moral‑judgment benchmark from Reddit AITA posts and a text‑based interactive decision‑making benchmark. The study finds that survey‑based supervision can change both survey responses and downstream behavior, but the transfer is strongly model‑ and value‑dependent and influenced by how value descriptions are framed.
arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.
VISTA is a method that steers large language models by inferring a latent user value profile from a few survey answers and applying a hypernetwork to generate user‑specific interventions. It outperforms or matches in‑context profiling on synthetic and real datasets (Food2D, World Values Survey, PrimeX) without adding prompt tokens. VISTA also adaptively selects which survey questions to ask, stopping after only 6% of the questionnaire while preserving 94% of the full‑history benefit, and identifies a clear value axis that accurately positions unseen users.
The paper investigates whether large language models (LLMs) possess intrinsic value systems and how to quantify and align them. By projecting responses from 106 LLMs and 95,000 human survey profiles into a shared sociological space, the authors confirm that LLMs do have values, though these values form a concentrated, idealized core rather than mirroring human diversity. They introduce the Prior-Environment-Cognition (PEC) framework to mathematically define value expression and propose an adaptive Alignment Prescription that identifies minimal interventions—ranging from prompts to targeted parameter updates—to steer LLM values efficiently without harming general performance.
arXiv:2602.11328v2 Announce Type: replace Abstract: As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral dis...
arXiv:2510. 12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide.
The paper examines how fine‑tuning large language models (LLMs) for specific human values can unintentionally affect other, untrained values and broader behaviors. By adapting four instruction‑tuned LLMs to Hofstede’s value dimensions, the authors evaluate cross‑value transfer and observe side effects on risk preferences, social cognition, and safety traits. They conclude that post‑training value alignment should include comprehensive behavioral assessments and that further research is needed to determine the optimal placement of value training within the development pipeline.
arXiv:2608.30902v1 Announce Type: new Abstract: Adapting large language models to user-specific preferences is often constrained by the cost of human annotation, making preference optimisation imprac...
arXiv:2606. 00334v1 Announce Type: cross Abstract: Various language domains have undergone remarkable changes in recent years; these shifts are largely attributed to the advent of Large Language Models and their misalignment with natural language usage.
arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.
arXiv:2510. 26707v2 Announce Type: replace-cross Abstract: As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to align with certain human value systems.
arXiv:2602. 03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to capture deeper motivational principles.
arXiv:2607. 16195v1 Announce Type: new Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF).