Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite little being known about how they map outcomes to feelings or use those mappings in choice. We tested how LLMs anticipated gambling or stock-trading outcomes would feel and whether they incorporated these ratings into choice. When outcomes were rated in isolation, ratings exhibited diminishing sensitivity to magnitude, resembling human value functions; but models rated gains more intensely than equivalent losses, an inversion of canonical loss aversion we call silicon gain amplification. When outcomes were rated together, enabling direct comparison, ratings linearised. In choice, the gain-loss asymmetry largely disappeared, and models'own ratings did not reliably explain their choices better than monetary utility did. These findings reveal malleable mappings from outcomes to affect, raising questions about whether and how agentic LLMs should incorporate anticipated feelings into decisions.
People increasingly use large language models (LLMs) to interpret others'behaviour, often assuming that LLMs infer motives as humans do. Aligned LLMs attribute more benevolent and less malevolent motives to a whistleblower than humans do, but ratings alone cannot reveal whether these differences arise in internal repre...
When a language model answers a sequence of questions, researchers report the model and temperature but rarely what or how it was told about its own earlier answers. I show that this underreported setting behaves as a mode effect. Testing four large language model (LLM) families with revealed preference theory (GARP)...
Yick Chung· Sociological Methods & R...· 0 citations
Large language models (LLMs) are increasingly used as experimental subjects in behavioral and economic research. However, it remains unclear whether their decisions reflect stable preferences or context-dependent responses. This study examines intertemporal choice behavior in LLMs within a controlled experimental frame...
Ali Kemal Başbuğ· Akademik yaklaşımlar dergisi· 0 citations
Understanding people often requires inferring what they feel or want from how they behave rather than from what they state outright. We ask whether language models can make these inferences, and how they do so. We introduce Theory-of-Mind Stories (ToMStories), an evaluation dataset of ~6,000 short synthetically generat...
Evaluating LLMs in interaction with humans and designing support for selective deference that preserves independent reasoning motivate evaluating LLMs in interaction with humans.
Robin Welsch, Michelle Rausch, Pascal Knierim et al.· 0 citations
Although large language models support a wide range of tasks, hallucinations remain a major obstacle to user trust. When a model generates an answer, both its internal processing and final output vary with the question. Internal states are often expected to offer richer signals for hallucination detection. However, stu...
