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The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models
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In this paper, we address the concept of "alignment" in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its parallels to empty signifiers. To establish a shared vocabulary around how abstract concepts of alignment are operationalised in empirical datasets, we propose a framework that demarcates: 1) which dimensions of model behaviour are considered important, then 2) how meanings and definitions are ascribed to these dimensions, and by whom. We situate existing empirical literature and provide guidance on deciding which paradigm to follow. Through this framework, we aim to foster a culture of transparency and critical evaluation, aiding the community in navigating the complexities of aligning LLMs with human populations.
Forward citations
Cited by 4 Pith papers
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Toward a Theory of Value in AI Alignment
A systematic annotation of 94 AI alignment papers shows the field largely equates human values with measurable preferences, rarely defines values, and is increasingly removing humans from alignment evaluation.
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Structural transparency of societal AI alignment through Institutional Logics
Introduces a five-component analytical framework, grounded in Institutional Logics, for making visible the organizational and institutional decisions that shape AI alignment.
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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models
Seed2Harvest expands 1,000 human adversarial prompts into 27,650 LLM-generated variants that keep roughly comparable unsafe-image trigger rates and add hundreds of new geographic contexts.
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Scopes of Alignment
Alignment should be defined by three scopes, competence, transience, and audience, rather than by generic context-free values.
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