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
- 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) - Explicit and Implicit Large Language Model Personas Generate Opinions but Fail to Replicate Deeper Perceptions and Biases [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? [103.67867531892988]
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) - Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs [67.51906565969227]
We study the unintended side-effects of persona assignment on the ability of LLMs to perform basic reasoning tasks.
Our study covers 24 reasoning datasets, 4 LLMs, and 19 diverse personas (e.g. an Asian person) spanning 5 socio-demographic groups.
arXiv Detail & Related papers (2023-11-08T18:52:17Z) - 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) - POSQA: Probe the World Models of LLMs with Size Comparisons [38.30479784257936]
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain.
With the explosive growth of Large Language Models (LLMs) and their already ubiquitous presence in our daily lives, it is becoming increasingly necessary to verify their real-world understanding.
arXiv Detail & Related papers (2023-10-20T10:05:01Z) - Investigating Large Language Models' Perception of Emotion Using
Appraisal Theory [3.0902630634005797]
Large Language Models (LLM) have significantly advanced in recent years and are now being used by the general public.
In this work, we investigate their emotion perception through the lens of appraisal and coping theory.
We applied SCPQ to three recent LLMs from OpenAI, davinci-003, ChatGPT, and GPT-4 and compared the results with predictions from the appraisal theory and human data.
arXiv Detail & Related papers (2023-10-03T16:34:47Z) - Exploring the psychology of LLMs' Moral and Legal Reasoning [0.0]
Large language models (LLMs) exhibit expert-level performance in tasks across a wide range of different domains.
Ethical issues raised by LLMs and the need to align future versions makes it important to know how state of the art models reason about moral and legal issues.
We replicate eight studies from the experimental literature with instances of Google's Gemini Pro, Anthropic's Claude 2.1, OpenAI's GPT-4, and Meta's Llama 2 Chat 70b.
We find that alignment with human responses shifts from one experiment to another, and that models differ amongst themselves as to their overall
arXiv Detail & Related papers (2023-08-02T16:36:58Z) - 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.