How Inclusively do LMs Perceive Social and Moral Norms?

• URL: http://arxiv.org/abs/2502.02696v1
• Date: Tue, 04 Feb 2025 20:24:17 GMT
• Title: How Inclusively do LMs Perceive Social and Moral Norms?
• Authors: Michael Galarnyk, Agam Shah, Dipanwita Guhathakurta, Poojitha Nandigam, Sudheer Chava,
• Abstract summary: Language models (LMs) are used in decision-making systems and as interactive assistants. We investigate how inclusively LMs perceive norms across demographic groups. We find notable disparities in LM responses, with younger, higher-income groups showing closer alignment.
• Score: 5.302888878095751
• License:
• Abstract: This paper discusses and contains offensive content. Language models (LMs) are used in decision-making systems and as interactive assistants. However, how well do these models making judgements align with the diversity of human values, particularly regarding social and moral norms? In this work, we investigate how inclusively LMs perceive norms across demographic groups (e.g., gender, age, and income). We prompt 11 LMs on rules-of-thumb (RoTs) and compare their outputs with the existing responses of 100 human annotators. We introduce the Absolute Distance Alignment Metric (ADA-Met) to quantify alignment on ordinal questions. We find notable disparities in LM responses, with younger, higher-income groups showing closer alignment, raising concerns about the representation of marginalized perspectives. Our findings highlight the importance of further efforts to make LMs more inclusive of diverse human values. The code and prompts are available on GitHub under the CC BY-NC 4.0 license.

Related papers

• 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)
• Language Model Alignment in Multilingual Trolley Problems [138.5684081822807]
  Building on the Moral Machine experiment, we develop a cross-lingual corpus of moral dilemma vignettes in over 100 languages called MultiTP. Our analysis explores the alignment of 19 different LLMs with human judgments, capturing preferences across six moral dimensions. We discover significant variance in alignment across languages, challenging the assumption of uniform moral reasoning in AI systems.
  arXiv  Detail & Related papers  (2024-07-02T14:02:53Z)
• Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models [61.45529177682614]
  We challenge the prevailing constrained evaluation paradigm for values and opinions in large language models. We show that models give substantively different answers when not forced. We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
  arXiv  Detail & Related papers  (2024-02-26T18:00:49Z)
• Whose Emotions and Moral Sentiments Do Language Models Reflect? [5.4547979989237225]
  Language models (LMs) are known to represent the perspectives of some social groups better than others. We find significant misalignment of LMs with both ideological groups. Even after steering the LMs towards specific ideological perspectives, the misalignment and liberal tendencies of the model persist.
  arXiv  Detail & Related papers  (2024-02-16T22:34:53Z)
• Assessing LLMs for Moral Value Pluralism [2.860608352191896]
  We utilize a Recognizing Value Resonance (RVR) NLP model to identify World Values Survey (WVS) values that resonate and conflict with a given passage of text. We find that LLMs exhibit several Western-centric value biases. Our results highlight value misalignment and age groups, and a need for social science informed technological solutions.
  arXiv  Detail & Related papers  (2023-12-08T16:18:15Z)
• MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks [49.60689355674541]
  A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments. We test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with human participants.
  arXiv  Detail & Related papers  (2023-10-30T15:57:32Z)
• Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
  Large Language Models (LLMs) have made it crucial to align their values with those of humans. We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
  arXiv  Detail & Related papers  (2023-05-26T02:34:20Z)
• Whose Opinions Do Language Models Reflect? [88.35520051971538]
  We investigate the opinions reflected by language models (LMs) by leveraging high-quality public opinion polls and their associated human responses. We find substantial misalignment between the views reflected by current LMs and those of US demographic groups. Our analysis confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs.
  arXiv  Detail & Related papers  (2023-03-30T17:17:08Z)
• Discovering Language Model Behaviors with Model-Written Evaluations [18.24267922379281]
  As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Here, we automatically generate evaluations with LMs. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size.
  arXiv  Detail & Related papers  (2022-12-19T05:13:52Z)
• Language Models have a Moral Dimension [13.991481870016631]
  We show that recent improvements of LMs store ethical and moral values of the society. This provides a path for attenuating or even preventing toxic degeneration in LMs.
  arXiv  Detail & Related papers  (2021-03-08T16:59:52Z)

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.