Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915491162882048 |
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| author | Peter, Oriane Devlin, Kate |
| author_facet | Peter, Oriane Devlin, Kate |
| contents | Large Language Models (LLMs) alignment methods have been credited with the commercial success of products like ChatGPT, given their role in steering LLMs towards user-friendly outputs. However, current alignment techniques predominantly mirror the normative preferences of a narrow reference group, effectively imposing their values on a wide user base. Drawing on theories of the power/knowledge nexus, this work argues that current alignment practices centralise control over knowledge production and governance within already influential institutions. To counter this, we propose decentralising alignment through three characteristics: context, pluralism, and participation. Furthermore, this paper demonstrates the critical importance of delineating the context-of-use when shaping alignment practices by grounding each of these features in concrete use cases. This work makes the following contributions: (1) highlighting the role of context, pluralism, and participation in decentralising alignment; (2) providing concrete examples to illustrate these strategies; and (3) demonstrating the nuanced requirements associated with applying alignment across different contexts of use. Ultimately, this paper positions LLM alignment as a potential site of resistance against epistemic injustice and the erosion of democratic processes, while acknowledging that these strategies alone cannot substitute for broader societal changes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation Peter, Oriane Devlin, Kate Computers and Society Machine Learning Large Language Models (LLMs) alignment methods have been credited with the commercial success of products like ChatGPT, given their role in steering LLMs towards user-friendly outputs. However, current alignment techniques predominantly mirror the normative preferences of a narrow reference group, effectively imposing their values on a wide user base. Drawing on theories of the power/knowledge nexus, this work argues that current alignment practices centralise control over knowledge production and governance within already influential institutions. To counter this, we propose decentralising alignment through three characteristics: context, pluralism, and participation. Furthermore, this paper demonstrates the critical importance of delineating the context-of-use when shaping alignment practices by grounding each of these features in concrete use cases. This work makes the following contributions: (1) highlighting the role of context, pluralism, and participation in decentralising alignment; (2) providing concrete examples to illustrate these strategies; and (3) demonstrating the nuanced requirements associated with applying alignment across different contexts of use. Ultimately, this paper positions LLM alignment as a potential site of resistance against epistemic injustice and the erosion of democratic processes, while acknowledging that these strategies alone cannot substitute for broader societal changes. |
| title | Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2509.08858 |