Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks

• URL: http://arxiv.org/abs/2406.17232v2
• Date: Wed, 16 Oct 2024 04:36:09 GMT
• Title: Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks
• Authors: Yun-Shiuan Chuang, Krirk Nirunwiroj, Zach Studdiford, Agam Goyal, Vincent V. Frigo, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers,
• Abstract summary: Using data from a human survey, we estimated a belief network encompassing 64 topics loading on nine non-overlapping latent factors. We then seeded LLM-based agents with an opinion on one topic, and assessed the alignment of its expressed opinions on remaining test topics with corresponding human data. Role-playing based on demographic information alone did not align LLM and human opinions, but seeding the agent with a single belief greatly improved alignment for topics related in the belief network, and not for topics outside the network.
• Score: 5.76230391989518
• License:
• Abstract: Creating human-like large language model (LLM) agents is crucial for faithful social simulation. Having LLMs role-play based on demographic information sometimes improves human likeness but often does not. This study assessed whether LLM alignment with human behavior can be improved by integrating information from empirically-derived human belief networks. Using data from a human survey, we estimated a belief network encompassing 64 topics loading on nine non-overlapping latent factors. We then seeded LLM-based agents with an opinion on one topic, and assessed the alignment of its expressed opinions on remaining test topics with corresponding human data. Role-playing based on demographic information alone did not align LLM and human opinions, but seeding the agent with a single belief greatly improved alignment for topics related in the belief network, and not for topics outside the network. These results suggest a novel path for human-LLM belief alignment in work seeking to simulate and understand patterns of belief distributions in society.

Related papers

• NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews [65.35458530702442]
  We focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two-person informational interviews from NPR and CNN. LLMs are significantly less likely than human interviewers to use acknowledgements and to pivot to higher-level questions.
  arXiv  Detail & Related papers  (2024-11-21T01:37:38Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Agentic Society: Merging skeleton from real world and texture from Large Language Model [4.740886789811429]
  This paper explores a novel framework that leverages census data and large language models to generate virtual populations. We show that our method produces personas with variability essential for simulating diverse human behaviors in social science experiments. But the evaluation result shows that only weak sign of statistical truthfulness can be produced due to limited capability of current LLMs.
  arXiv  Detail & Related papers  (2024-09-02T08:28:19Z)
• 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)
• From Persona to Personalization: A Survey on Role-Playing Language Agents [52.783043059715546]
  Recent advancements in large language models (LLMs) have boosted the rise of Role-Playing Language Agents (RPLAs) RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. They have catalyzed numerous AI applications, such as emotional companions, interactive video games, personalized assistants and copilots.
  arXiv  Detail & Related papers  (2024-04-28T15:56:41Z)
• Can Large Language Model Agents Simulate Human Trust Behavior? [81.45930976132203]
  We investigate whether Large Language Model (LLM) agents can simulate human trust behavior. GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior. We also probe the biases of agent trust and differences in agent trust towards other LLM agents and humans.
  arXiv  Detail & Related papers  (2024-02-07T03:37:19Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases. These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents [7.986590413263814]
  "Wisdom of partisan crowds" is a phenomenon known as the "wisdom of partisan crowds" We find that partisan crowds display human-like partisan biases, but also converge to more accurate beliefs through deliberation as humans do. We identify several factors that interfere with convergence, including the use of chain-of-thought prompt and lack of details in personas.
  arXiv  Detail & Related papers  (2023-11-16T08:30:15Z)
• Simulating Opinion Dynamics with Networks of LLM-based Agents [7.697132934635411]
  We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs) Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research.
  arXiv  Detail & Related papers  (2023-11-16T07:01:48Z)
• On the steerability of large language models toward data-driven personas [98.9138902560793]
  Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to achieve controllable generation of specific viewpoints using LLMs.
  arXiv  Detail & Related papers  (2023-11-08T19:01:13Z)
• 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)

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.