| Challenge: | Existing work has failed to acknowledge that what counts as a rationale is subjective. |
| Approach: | They propose to use demographic annotations to augment existing datasets to ask what demographics our models align with and whose reasoning patterns they align with. |
| Outcome: | The proposed model rationales align better with older and/or white annotators, and are biased towards older and white anorators. |
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| Challenge: | large language models (LLMs) increasingly assist subjective decision-making . prior work uses aggregate human judgments, but demographic variation and its linguistic drivers remain underexplored. |
| Approach: | They analyze how demographic background and empathy level correlate with LLM-generated dilemma responses . they also identify markers that predict group-level differences . |
| Outcome: | The authors show that demographic background and empathy level correlate with LLM preferences . their findings highlight the need for demographically informed LLM evaluations. |
You Are What You Annotate: Towards Better Models through Annotator Representations (2023.findings-emnlp)
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| Challenge: | Annotator disagreement is ubiquitous in natural language processing tasks. |
| Approach: | They propose to model annotators' idiosyncrasies and account for their idioms by creating representations for each annotator and their annotations. |
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Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives (2026.acl-long)
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| Challenge: | Recent work treats disagreements as signal, instead of noise, resulting in a single label and marginalizing minoritized perspectives. |
| Approach: | They propose an approach to modeling annotator disagreement in subjective NLP tasks through architectural and data-centric innovations. |
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Aligning Language Models to User Opinions (2023.findings-emnlp)
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| Challenge: | Personality is a defining feature of human beings, shaped by a complex interplay of demographic characteristics, moral principles, and social experiences. |
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Persona Prompting as a Lens on LLM Social Reasoning (2026.eacl-long)
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Jing Yang, Moritz Hechtbauer, Elisabeth Khalilov, Evelyn Luise Brinkmann, Vera Schmitt, Nils Feldhus
| Challenge: | Persona prompting (PP) is increasingly used to steer large language models towards user-specific generation, but its effect on rationales remains underexplored. |
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Can Language Models Reason about Individualistic Human Values and Preferences? (2025.acl-long)
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| Challenge: | Existing methods and evaluation frameworks for achieving pluralistic alignment are limited by the diversity of people, which is pre-specified and coarsely categorized, papering over individuality. |
| Approach: | They propose to use a dataset transformed from the influential World Values Survey to study language models on the specific challenge of individualistic value reasoning. |
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Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness (2025.findings-acl)
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| Challenge: | despite evidence of demographic bias, reports with whom they align best are hard to generalize or contradictory . confounders introduced in the annotation process account for more variation in alignment patterns than demographic traits . |
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Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback (2024.acl-long)
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| Challenge: | a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences is focusing on the setting of fairness in content moderation. |
| Approach: | They propose to use human feedback to determine how two comments should be treated in content moderation to learn about human values and preferences. |
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Proposal: From One-Fit-All to Perspective Aware Modeling (2025.acl-srw)
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| Challenge: | Variation in human annotation and human perspectives has drawn increasing attention in natural language processing research. |
| Approach: | They propose to use annotation formats that better capture granularity and uncertainty of individual judgments and annotation modeling that leverages socio-demographic features to better represent and predict underrepresented or minority perspectives. |
| Outcome: | The proposed tasks aim to advance natural language processing research towards more faithfully reflecting the diversity of human interpretation, enhancing both inclusiveness and fairness in language technologies. |
Which Demographics do LLMs Default to During Annotation? (2025.acl-long)
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Johannes Schäfer, Aidan Combs, Christopher Bagdon, Jiahui Li, Nadine Probol, Lynn Greschner, Sean Papay, Yarik Menchaca Resendiz, Aswathy Velutharambath, Amelie Wuehrl, Sabine Weber, Roman Klinger
| Challenge: | Demographics and cultural background of annotators influence the labels they assign in text annotation. |
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