arXiv AI
A Global View of How LLMs Value Human Time
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arXiv AI
Oct 27
Integrating Large Language Models with Human Data to Design Personalized Decoy Nudges
arXiv:2508.12045v3 Announce Type: replace-cross Abstract: Although nudge strategies can influence behavior, their effectiveness varies across individuals, highlighting the need for personalized nudge...
arXiv AI
Jun 17
Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices
arXiv:2602. 09802v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in applications such as travel assistance and purchasing support, they are often required to make subjective choices on behalf of users in settings where no objectively correct answer exists.
Hugging Face Trending Papers
Oct 5
Synthetic Cultural Agents from Aggregate Anchors
The paper proposes a new method for generating synthetic survey responses that uses declared aggregate preference anchors to create group-indexed choice policies, avoiding the mixing of inference-time information with pretraining associations. It introduces a parameter‑efficient adapter trained via Direct Preference Optimization to map GPS coordinates to synthetic responses, and evaluates these adapters on World Values Survey items, showing they recover imposed labels and exhibit strong correlation with GPS trust scores. The study demonstrates that anchored policies can preserve aggregate signals while not necessarily mirroring human response patterns, offering an inspectable construction and evaluation framework that separates anchor transfer from human criterion agreement.
arXiv Computation and Language
Oct 15
ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models
arXiv:2610.20472v1 Announce Type: new Abstract: Cultural attitudes and social norms evolve over time, yet most cultural benchmarks for large language models (LLMs) treat culture as a static attribute...
arXiv AI
2d ago
Decision Models as Scalable Economic Surrogates that Capture Human Variation
Decision Models (DMs) are a new class of AI models that provide fast responses and output a probability distribution over a fixed set of options. In a first evaluation, 10 DMs were compared with 8 Large Language Models (LLMs) across three economic settings—a household spending survey, classic economic games, and a labour‑market field experiment. Frontier DMs, those scoring highest on general decision‑making benchmarks, reproduced human response variation more closely than LLMs in all settings, matched LLMs on modal responses in the spending survey, and were roughly ten times faster and over a hundred times cheaper than the fastest LLM.
arXiv AI
Sep 3
Accurate in space, unreliable in time: how LLMs represent national cultural change
The paper investigates how large language models (LLMs) represent national cultural change over time, using more than two decades of World Values Survey data and the Inglehart‑Welzel cultural map. It finds that while LLMs generally place countries near their most recent surveyed positions, their representations lag behind current data, under‑capture the magnitude of change, introduce spurious movements, and rarely reproduce trajectory reversals. These temporal inaccuracies reveal a flattening effect that limits the models’ cultural awareness and raises concerns for evaluation, representational harms, and governance of culturally aware AI systems.