Political Bias in LLMs: Unaligned Moral Values in Agent-centric Simulations

• URL: http://arxiv.org/abs/2408.11415v2
• Date: Mon, 14 Jul 2025 08:34:57 GMT
• Title: Political Bias in LLMs: Unaligned Moral Values in Agent-centric Simulations
• Authors: Simon Münker,
• Abstract summary: We investigate how personalized language models align with human responses on the Moral Foundation Theory Questionnaire.<n>We adapt open-source generative language models to different political personas and repeatedly survey these models to generate synthetic data sets.<n>Our analysis reveals that models produce inconsistent results across multiple repetitions, yielding high response variance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary research in social sciences increasingly utilizes state-of-the-art generative language models to annotate or generate content. While these models achieve benchmark-leading performance on common language tasks, their application to novel out-of-domain tasks remains insufficiently explored. To address this gap, we investigate how personalized language models align with human responses on the Moral Foundation Theory Questionnaire. We adapt open-source generative language models to different political personas and repeatedly survey these models to generate synthetic data sets where model-persona combinations define our sub-populations. Our analysis reveals that models produce inconsistent results across multiple repetitions, yielding high response variance. Furthermore, the alignment between synthetic data and corresponding human data from psychological studies shows a weak correlation, with conservative persona-prompted models particularly failing to align with actual conservative populations. These results suggest that language models struggle to coherently represent ideologies through in-context prompting due to their alignment process. Thus, using language models to simulate social interactions requires measurable improvements in in-context optimization or parameter manipulation to align with psychological and sociological stereotypes properly.

Related papers

• Do language models accommodate their users? A study of linguistic convergence [15.958711524171362]
  We find that models strongly converge to the conversation's style, often significantly overfitting relative to the human baseline.<n>We observe consistent shifts in convergence across modeling settings, with instruction-tuned and larger models converging less than their pretrained counterparts.
  arXiv  Detail & Related papers  (2025-08-05T09:55:40Z)
• Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
  We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
  arXiv  Detail & Related papers  (2025-07-16T18:01:03Z)
• Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings [7.284860523651357]
  We assess the misalignment between Large Language Models (LLMs)-simulated and actual human behaviors in multiple-choice survey settings.<n>We apply this framework to a popular language model for simulating people's opinions in various public surveys.<n>This raises questions about the alignment of this language model with the tested populations.
  arXiv  Detail & Related papers  (2025-06-17T22:04:55Z)
• Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning [77.120955854093]
  We show that data diversity can be a strong predictor of generalization in language models.<n>We introduce G-Vendi, a metric that quantifies diversity via the entropy of model-induced gradients.<n>We present Prismatic Synthesis, a framework for generating diverse synthetic data.
  arXiv  Detail & Related papers  (2025-05-26T16:05:10Z)
• Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction [5.774786149181393]
  We analyze how demographic attributes and prompt variations influence latent opinion mappings in large language models (LLMs)<n>We find that LLM-generated data fails to replicate the variance observed in real-world human responses.<n>In the political space, persona-to-party mappings exhibit limited differentiation, resulting in synthetic data that lacks the nuanced distribution of opinions found in survey data.
  arXiv  Detail & Related papers  (2025-02-22T16:25:33Z)
• From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models [17.04716417556556]
  This review visits foundational concepts such as the distributional hypothesis and contextual similarity. We examine both static and contextualized embeddings, underscoring advancements in models such as ELMo, BERT, and GPT. The discussion extends to sentence and document embeddings, covering aggregation methods and generative topic models. Advanced topics such as model compression, interpretability, numerical encoding, and bias mitigation are analyzed, addressing both technical challenges and ethical implications.
  arXiv  Detail & Related papers  (2024-11-06T15:40:02Z)
• 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)
• Computational Models to Study Language Processing in the Human Brain: A Survey [47.81066391664416]
  This paper reviews efforts in using computational models for brain research, highlighting emerging trends. Our analysis reveals that no single model outperforms others on all datasets.
  arXiv  Detail & Related papers  (2024-03-20T08:01:22Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• Feature Interactions Reveal Linguistic Structure in Language Models [2.0178765779788495]
  We study feature interactions in the context of feature attribution methods for post-hoc interpretability. We work out a grey box methodology, in which we train models to perfection on a formal language classification task. We show that under specific configurations, some methods are indeed able to uncover the grammatical rules acquired by a model.
  arXiv  Detail & Related papers  (2023-06-21T11:24:41Z)
• Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation [68.9440575276396]
  This survey aims to provide an overview of the recent research that has leveraged human feedback to improve natural language generation. First, we introduce an encompassing formalization of feedback, and identify and organize existing research into a taxonomy following this formalization. Second, we discuss how feedback can be described by its format and objective, and cover the two approaches proposed to use feedback (either for training or decoding): directly using the feedback or training feedback models. Third, we provide an overview of the nascent field of AI feedback, which exploits large language models to make judgments based on a set of principles and minimize the need for
  arXiv  Detail & Related papers  (2023-05-01T17:36:06Z)
• Chain of Hindsight Aligns Language Models with Feedback [62.68665658130472]
  We propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model. By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors.
  arXiv  Detail & Related papers  (2023-02-06T10:28:16Z)
• Are Neural Topic Models Broken? [81.15470302729638]
  We study the relationship between automated and human evaluation of topic models. We find that neural topic models fare worse in both respects compared to an established classical method.
  arXiv  Detail & Related papers  (2022-10-28T14:38:50Z)
• Out of One, Many: Using Language Models to Simulate Human Samples [3.278541277919869]
  We show that the "algorithmic bias" within one such tool -- the GPT-3 language model -- is both fine-grained and demographically correlated. We create "silicon samples" by conditioning the model on thousands of socio-demographic backstories from real human participants.
  arXiv  Detail & Related papers  (2022-09-14T19:53:32Z)
• Schr\"odinger's Tree -- On Syntax and Neural Language Models [10.296219074343785]
  Language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities. We observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form. We outline the implications of the different types of research questions exhibited in studies on syntax.
  arXiv  Detail & Related papers  (2021-10-17T18:25:23Z)
• Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? [62.74872383104381]
  We investigate the effectiveness of natural language interventions for reading-comprehension systems. We propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a question-answering (QA) model's unethical behavior.
  arXiv  Detail & Related papers  (2021-06-02T20:57:58Z)
• Evaluating the Interpretability of Generative Models by Interactive Reconstruction [30.441247705313575]
  We introduce a task to quantify the human-interpretability of generative model representations. We find performance on this task much more reliably differentiates entangled and disentangled models than baseline approaches.
  arXiv  Detail & Related papers  (2021-02-02T02:38:14Z)

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.