MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks

• URL: http://arxiv.org/abs/2310.19677v2
• Date: Tue, 31 Oct 2023 06:36:13 GMT
• Title: MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
• Authors: Allen Nie, Yuhui Zhang, Atharva Amdekar, Chris Piech, Tatsunori Hashimoto, Tobias Gerstenberg
• Abstract summary: A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments. We test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with human participants.
• Score: 49.60689355674541
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why? A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments, such as the violation of norms and whether the harm is avoidable or inevitable. We collected a dataset of stories from 24 cognitive science papers and developed a system to annotate each story with the factors they investigated. Using this dataset, we test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with those of human participants. On the aggregate level, alignment has improved with more recent LLMs. However, using statistical analyses, we find that LLMs weigh the different factors quite differently from human participants. These results show how curated, challenge datasets combined with insights from cognitive science can help us go beyond comparisons based merely on aggregate metrics: we uncover LLMs implicit tendencies and show to what extent these align with human intuitions.

Related papers

• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the causal reasoning abilities of large language models (LLMs) through the representative problem of inferring causal relationships from narratives. We find that even state-of-the-art language models rely on unreliable shortcuts, both in terms of the narrative presentation and their parametric knowledge.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology [13.964263002704582]
  We show that, even with the use of Chains of Thought prompts, mainstream LLMs have a high error rate when solving modified CRT problems. Specifically, the average accuracy rate dropped by up to 50% compared to the original questions. This finding challenges the belief that LLMs have genuine mathematical reasoning abilities comparable to humans.
  arXiv  Detail & Related papers  (2024-10-19T05:01:56Z)
• FairMindSim: Alignment of Behavior, Emotion, and Belief in Humans and LLM Agents Amid Ethical Dilemmas [23.26678104324838]
  We introduced FairMindSim, which simulates the moral dilemma through a series of unfair scenarios. We used LLM agents to simulate human behavior, ensuring alignment across various stages. Our findings indicate that, behaviorally, GPT-4o exhibits a stronger sense of social justice, while humans display a richer range of emotions.
  arXiv  Detail & Related papers  (2024-10-14T11:39:05Z)
• Investigating Context Effects in Similarity Judgements in Large Language Models [6.421776078858197]
  Large Language Models (LLMs) have revolutionised the capability of AI models in comprehending and generating natural language text. We report an ongoing investigation on alignment of LLMs with human judgements affected by order bias.
  arXiv  Detail & Related papers  (2024-08-20T10:26:02Z)
• Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models [57.518784855080334]
  Large Language Models (LLMs) have demonstrated exceptional task-solving capabilities, increasingly adopting roles akin to human-like assistants. This paper presents a framework for investigating psychology dimension in LLMs, including psychological identification, assessment dataset curation, and assessment with results validation. We introduce a comprehensive psychometrics benchmark for LLMs that covers six psychological dimensions: personality, values, emotion, theory of mind, motivation, and intelligence.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas [14.650234624251716]
  Large language models (LLMs) are increasingly being used in human-centered social scientific tasks. These tasks are highly subjective and dependent on human factors, such as one's environment, attitudes, beliefs, and lived experiences. We examine the role of prompting LLMs with human-like personas and ask the models to answer as if they were a specific human.
  arXiv  Detail & Related papers  (2024-06-20T16:24:07Z)
• What Evidence Do Language Models Find Convincing? [94.90663008214918]
  We build a dataset that pairs controversial queries with a series of real-world evidence documents that contain different facts. We use this dataset to perform sensitivity and counterfactual analyses to explore which text features most affect LLM predictions. Overall, we find that current models rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important.
  arXiv  Detail & Related papers  (2024-02-19T02:15:34Z)
• Do LLMs exhibit human-like response biases? A case study in survey design [66.1850490474361]
  We investigate the extent to which large language models (LLMs) reflect human response biases, if at all. We design a dataset and framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires. Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior.
  arXiv  Detail & Related papers  (2023-11-07T15:40:43Z)
• Moral Foundations of Large Language Models [6.6445242437134455]
  Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors. As large language models (LLMs) are trained on datasets collected from the internet, they may reflect the biases that are present in such corpora. This paper uses MFT as a lens to analyze whether popular LLMs have acquired a bias towards a particular set of moral values.
  arXiv  Detail & Related papers  (2023-10-23T20:05:37Z)
• Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns [51.622612759892775]
  Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs) LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
  arXiv  Detail & Related papers  (2023-05-08T16:10:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.