Large language models display the identifiable victim effect at roughly twice the human baseline, strongly amplified by instruction tuning and chain-of-thought prompting but inverted by reasoning-specialized models.
Royal Society Open Science11(6), 240255 (2024) https://doi
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LLMs encode accurate but brittle internal beliefs about latent game states and convert them poorly into actions, creating systematic gaps that explain strategic failures.
LLMs score 0.96 on standard probability exercises but 0.59 on counterintuitive ones and drop further with biased wording or misleading cues, indicating they are not genuine probabilistic reasoners.
citing papers explorer
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Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models
Large language models display the identifiable victim effect at roughly twice the human baseline, strongly amplified by instruction tuning and chain-of-thought prompting but inverted by reasoning-specialized models.
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Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions
LLMs encode accurate but brittle internal beliefs about latent game states and convert them poorly into actions, creating systematic gaps that explain strategic failures.
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How reliable are LLMs when it comes to playing dice?
LLMs score 0.96 on standard probability exercises but 0.59 on counterintuitive ones and drop further with biased wording or misleading cues, indicating they are not genuine probabilistic reasoners.