Cognitive phantoms in LLMs through the lens of latent variables

• URL: http://arxiv.org/abs/2409.15324v1
• Date: Fri, 6 Sep 2024 12:42:35 GMT
• Title: Cognitive phantoms in LLMs through the lens of latent variables
• Authors: Sanne Peereboom, Inga Schwabe, Bennett Kleinberg,
• Abstract summary: Large language models (LLMs) increasingly reach real-world applications, necessitating a better understanding of their behaviour. Recent studies administering psychometric questionnaires to LLMs report human-like traits in LLMs, potentially influencing behaviour. This approach suffers from a validity problem: it presupposes that these traits exist in LLMs and that they are measurable with tools designed for humans. This study investigates this problem by comparing latent structures of personality between humans and three LLMs using two validated personality questionnaires.
• Score: 0.3441021278275805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) increasingly reach real-world applications, necessitating a better understanding of their behaviour. Their size and complexity complicate traditional assessment methods, causing the emergence of alternative approaches inspired by the field of psychology. Recent studies administering psychometric questionnaires to LLMs report human-like traits in LLMs, potentially influencing LLM behaviour. However, this approach suffers from a validity problem: it presupposes that these traits exist in LLMs and that they are measurable with tools designed for humans. Typical procedures rarely acknowledge the validity problem in LLMs, comparing and interpreting average LLM scores. This study investigates this problem by comparing latent structures of personality between humans and three LLMs using two validated personality questionnaires. Findings suggest that questionnaires designed for humans do not validly measure similar constructs in LLMs, and that these constructs may not exist in LLMs at all, highlighting the need for psychometric analyses of LLM responses to avoid chasing cognitive phantoms. Keywords: large language models, psychometrics, machine behaviour, latent variable modeling, validity

Related papers

• Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia [27.650551131885152]
  Research into large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest that powerful LLMs, like GPT-4, are beginning to exhibit human-like cognitive abilities.
  arXiv  Detail & Related papers  (2024-10-02T15:47:25Z)
• LLM Internal States Reveal Hallucination Risk Faced With a Query [62.29558761326031]
  Humans have a self-awareness process that allows us to recognize what we don't know when faced with queries. This paper investigates whether Large Language Models can estimate their own hallucination risk before response generation. By a probing estimator, we leverage LLM self-assessment, achieving an average hallucination estimation accuracy of 84.32% at run time.
  arXiv  Detail & Related papers  (2024-07-03T17:08:52Z)
• 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)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models [3.692410936160711]
  This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Using projective tests, we uncover hidden aspects of LLM personalities that are not easily accessible through direct questioning. Our machine learning analysis revealed that LLMs exhibit distinct AInality traits and manifest diverse personality types, demonstrating dynamic shifts in response to external instructions.
  arXiv  Detail & Related papers  (2023-12-21T04:57:21Z)
• Psychometric Predictive Power of Large Language Models [32.31556074470733]
  We find that instruction tuning does not always make large language models human-like from a cognitive modeling perspective. Next-word probabilities estimated by instruction-tuned LLMs are often worse at simulating human reading behavior than those estimated by base LLMs.
  arXiv  Detail & Related papers  (2023-11-13T17:19:14Z)
• Challenging the Validity of Personality Tests for Large Language Models [2.9123921488295768]
  Large language models (LLMs) behave increasingly human-like in text-based interactions. LLMs' responses to personality tests systematically deviate from human responses.
  arXiv  Detail & Related papers  (2023-11-09T11:54:01Z)
• 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)
• Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
  This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.