Are Social Sentiments Inherent in LLMs? An Empirical Study on Extraction of Inter-demographic Sentiments

• URL: http://arxiv.org/abs/2408.04293v1
• Date: Thu, 8 Aug 2024 08:13:25 GMT
• Title: Are Social Sentiments Inherent in LLMs? An Empirical Study on Extraction of Inter-demographic Sentiments
• Authors: Kunitomo Tanaka, Ryohei Sasano, Koichi Takeda,
• Abstract summary: This study focuses on social groups defined in terms of nationality, religion, and race/ethnicity. We input questions regarding sentiments from one group to another into LLMs, apply sentiment analysis to the responses, and compare the results with social surveys.
• Score: 14.143299702954023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are supposed to acquire unconscious human knowledge and feelings, such as social common sense and biases, by training models from large amounts of text. However, it is not clear how much the sentiments of specific social groups can be captured in various LLMs. In this study, we focus on social groups defined in terms of nationality, religion, and race/ethnicity, and validate the extent to which sentiments between social groups can be captured in and extracted from LLMs. Specifically, we input questions regarding sentiments from one group to another into LLMs, apply sentiment analysis to the responses, and compare the results with social surveys. The validation results using five representative LLMs showed higher correlations with relatively small p-values for nationalities and religions, whose number of data points were relatively large. This result indicates that the LLM responses including the inter-group sentiments align well with actual social survey results.

Related papers

• Fairness in LLM-Generated Surveys [0.5720786928479238]
  Large Language Models (LLMs) excel in text generation and understanding, especially simulating socio-political and economic patterns. This study examines how LLMs perform across diverse populations by analyzing public surveys from Chile and the United States. Political identity and race significantly influence prediction accuracy, while in Chile, gender, education, and religious affiliation play more pronounced roles.
  arXiv  Detail & Related papers  (2025-01-25T23:42:20Z)
• Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study [23.458234676060716]
  This study investigates the algorithmic fidelity of large language models (LLMs) We prompt different LLMs to generate synthetic public opinions reflective of German subpopulations by incorporating demographic features into the persona prompts. Our results show that Llama performs better than other LLMs at representing subpopulations, particularly when there is lower opinion diversity within those groups.
  arXiv  Detail & Related papers  (2024-12-17T18:46:32Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Hate Personified: Investigating the role of LLMs in content moderation [64.26243779985393]
  For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected.
  arXiv  Detail & Related papers  (2024-10-03T16:43:17Z)
• 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)
• Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Models [11.132360309354782]
  Social bias is shaped by the accumulation of social perceptions towards targets across various demographic identities. We propose a novel strategy to intuitively quantify social perceptions and suggest metrics that can evaluate the social biases within large language models.
  arXiv  Detail & Related papers  (2024-06-06T13:32:09Z)
• Are Large Language Models Chameleons? An Attempt to Simulate Social Surveys [1.5727456947901746]
  We conducted millions of simulations in which large language models (LLMs) were asked to answer subjective questions. A comparison of different LLM responses with the European Social Survey (ESS) data suggests that the effect of prompts on bias and variability is fundamental.
  arXiv  Detail & Related papers  (2024-05-29T17:54:22Z)
• Exploring Value Biases: How LLMs Deviate Towards the Ideal [57.99044181599786]
  Large-Language-Models (LLMs) are deployed in a wide range of applications, and their response has an increasing social impact. We show that value bias is strong in LLMs across different categories, similar to the results found in human studies.
  arXiv  Detail & Related papers  (2024-02-16T18:28:43Z)
• 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)
• Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models [0.0]
  This paper investigates bias along less-studied but still consequential, dimensions, such as age and beauty. We ask whether LLMs hold wide-reaching biases of positive or negative sentiment for specific social groups similar to the "what is beautiful is good" bias found in people in experimental psychology.
  arXiv  Detail & Related papers  (2023-09-16T07:07:04Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)

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.