Large language models as linguistic simulators and cognitive models in human research

• URL: http://arxiv.org/abs/2402.04470v4
• Date: Sun, 20 Oct 2024 16:13:29 GMT
• Title: Large language models as linguistic simulators and cognitive models in human research
• Authors: Zhicheng Lin,
• Abstract summary: The rise of large language models (LLMs) that generate human-like text has sparked debates over their potential to replace human participants in behavioral and cognitive research. We critically evaluate this replacement perspective to appraise the fundamental utility of language models in psychology and social science. This perspective reframes the role of language models in behavioral and cognitive science, serving as linguistic simulators and cognitive models that shed light on the similarities and differences between machine intelligence and human cognition and thoughts.
• Score: 0.0
• License:
• Abstract: The rise of large language models (LLMs) that generate human-like text has sparked debates over their potential to replace human participants in behavioral and cognitive research. We critically evaluate this replacement perspective to appraise the fundamental utility of language models in psychology and social science. Through a five-dimension framework, characterization, representation, interpretation, implication, and utility, we identify six fallacies that undermine the replacement perspective: (1) equating token prediction with human intelligence, (2) assuming LLMs represent the average human, (3) interpreting alignment as explanation, (4) anthropomorphizing AI, (5) essentializing identities, and (6) purporting LLMs as primary tools that directly reveal the human mind. Rather than replacement, the evidence and arguments are consistent with a simulation perspective, where LLMs offer a new paradigm to simulate roles and model cognitive processes. We highlight limitations and considerations about internal, external, construct, and statistical validity, providing methodological guidelines for effective integration of LLMs into psychological research, with a focus on model selection, prompt design, interpretation, and ethical considerations. This perspective reframes the role of language models in behavioral and cognitive science, serving as linguistic simulators and cognitive models that shed light on the similarities and differences between machine intelligence and human cognition and thoughts.

Related papers

• Cross-lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models [16.0617753653454]
  This study presents a comparative analysis between human performance and SSL models. We also compare the SER ability of models and humans at both utterance- and segment-levels. Our findings reveal that models, with appropriate knowledge transfer, can adapt to the target language and achieve performance comparable to native speakers.
  arXiv  Detail & Related papers  (2024-09-25T13:27:17Z)
• Theoretical and Methodological Framework for Studying Texts Produced by Large Language Models [0.0]
  This paper addresses the conceptual, methodological and technical challenges in studying large language models (LLMs) It builds on a theoretical framework that distinguishes between the LLM as a substrate and the entities the model simulates.
  arXiv  Detail & Related papers  (2024-08-29T17:34:10Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Human-like object concept representations emerge naturally in multimodal large language models [24.003766123531545]
  We combined behavioral and neuroimaging analysis methods to uncover how the object concept representations in Large Language Models correlate with those of humans. The resulting 66-dimensional embeddings were found to be highly stable and predictive, and exhibited semantic clustering akin to human mental representations. This study advances our understanding of machine intelligence and informs the development of more human-like artificial cognitive systems.
  arXiv  Detail & Related papers  (2024-07-01T08:17:19Z)
• Human Simulacra: Benchmarking the Personification of Large Language Models [38.21708264569801]
  Large language models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This paper introduces a framework for constructing virtual characters' life stories from the ground up. Experimental results demonstrate that our constructed simulacra can produce personified responses that align with their target characters.
  arXiv  Detail & Related papers  (2024-02-28T09:11:14Z)
• Is Cognition and Action Consistent or Not: Investigating Large Language Model's Personality [12.162460438332152]
  We investigate the reliability of Large Language Models (LLMs) in professing human-like personality traits through responses to personality questionnaires. Our goal is to evaluate the consistency between LLMs' professed personality inclinations and their actual "behavior" We propose hypotheses for the observed results based on psychological theories and metrics.
  arXiv  Detail & Related papers  (2024-02-22T16:32:08Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs [50.32802502923367]
  We study the process of language driving and influencing social reasoning in a probabilistic goal inference domain. We propose a neuro-symbolic model that carries out goal inference from linguistic inputs of agent scenarios. Our model closely matches human response patterns and better predicts human judgements than using an LLM alone.
  arXiv  Detail & Related papers  (2023-06-25T19:38:01Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• Evaluating and Inducing Personality in Pre-trained Language Models [78.19379997967191]
  We draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors. MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. We devise a Personality Prompting (P2) method to induce LLMs with specific personalities in a controllable way.
  arXiv  Detail & Related papers  (2022-05-20T07:32:57Z)

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.