title: Large Language Models Assume People are More Rational than We Really are
summary: In order for AI systems to communicate effectively with people, they must
  understand how we make decisions. However, people's decisions are not always
  rational, so the implicit internal models of human decision-making in Large
  Language Models (LLMs) must account for this. Previous empirical evidence seems
  to suggest that these implicit models are accurate -- LLMs offer believable
  proxies of human behavior, acting how we expect humans would in everyday
  interactions. However, by comparing LLM behavior and predictions to a large
  dataset of human decisions, we find that this is actually not the case: when
  both simulating and predicting people's choices, a suite of cutting-edge LLMs
  (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more
  rational than we really are. Specifically, these models deviate from human
  behavior and align more closely with a classic model of rational choice --
  expected value theory. Interestingly, people also tend to assume that other
  people are rational when interpreting their behavior. As a consequence, when we
  compare the inferences that LLMs and people draw from the decisions of others
  using another psychological dataset, we find that these inferences are highly
  correlated. Thus, the implicit decision-making models of LLMs appear to be
  aligned with the human expectation that other people will act rationally,
  rather than with how people actually act.
paper_authors._id: 667c2baf18aae25ed3a139fd
paper_authors.name: Ryan Liu
paper_authors._id: 667c2baf18aae25ed3a139fe
paper_authors.name: Jiayi Geng
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-26T14:54:42.114Z
paper_authors._id: 650f4ae42a45730c3fedf001
paper_authors.avatarUrl: /avatars/fc9274aeb0dc2071b120acfe52023bbe.svg
paper_authors.fullname: Jiayi Geng
paper_authors.type: user
paper_authors.user: JiayiG
paper_authors._id: 667c2baf18aae25ed3a139ff
paper_authors.name: Joshua C. Peterson
paper_authors._id: 667c2baf18aae25ed3a13a00
paper_authors.name: Ilia Sucholutsky
paper_authors._id: 667c2baf18aae25ed3a13a01
paper_authors.name: Thomas L. Griffiths
paper_title: Large Language Models Assume People are More Rational than We Really are
paper_summary: In order for AI systems to communicate effectively with people, they must
  understand how we make decisions. However, people's decisions are not always
  rational, so the implicit internal models of human decision-making in Large
  Language Models (LLMs) must account for this. Previous empirical evidence seems
  to suggest that these implicit models are accurate -- LLMs offer believable
  proxies of human behavior, acting how we expect humans would in everyday
  interactions. However, by comparing LLM behavior and predictions to a large
  dataset of human decisions, we find that this is actually not the case: when
  both simulating and predicting people's choices, a suite of cutting-edge LLMs
  (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more
  rational than we really are. Specifically, these models deviate from human
  behavior and align more closely with a classic model of rational choice --
  expected value theory. Interestingly, people also tend to assume that other
  people are rational when interpreting their behavior. As a consequence, when we
  compare the inferences that LLMs and people draw from the decisions of others
  using another psychological dataset, we find that these inferences are highly
  correlated. Thus, the implicit decision-making models of LLMs appear to be
  aligned with the human expectation that other people will act rationally,
  rather than with how people actually act.
paper_ai_summary: LLMs tend to overly assume rational decision-making in people, aligning their predictions more closely with expected value theory than actual human behavior.
paper_ai_keywords: Large Language Models LLMs GPT Llama Claude expected value theory