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Autores principales: Khan, Ishmam, Thogarrati, Sindhuja, Zhang, Shuo
Formato: Preprint
Publicado: 2026
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Acceso en línea:https://arxiv.org/abs/2605.11483
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author Khan, Ishmam
Thogarrati, Sindhuja
Zhang, Shuo
author_facet Khan, Ishmam
Thogarrati, Sindhuja
Zhang, Shuo
contents While large language models excel at factual adaptation, their ability to internalize nuanced philosophical frameworks under severe data constraints remains underexplored. We investigate this by specializing small LLMs on micro-datasets of foundational Stoic texts using preference optimization (ORPO, AlphaPO). Evaluated via a multi-model critic bank, our results show that just 300 high-fidelity examples can induce strong alignment with inward-facing Stoic virtues, closely approaching few-shot prompting while freeing the context window. Critically, however, all models, including few-shot baselines, exhibit a persistent failure on Stoicism's outward-facing cosmopolitan duties, pointing to a representational limitation of small models that micro-dataset adaptation alone cannot overcome.
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institution arXiv
publishDate 2026
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spellingShingle StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models
Khan, Ishmam
Thogarrati, Sindhuja
Zhang, Shuo
Computation and Language
While large language models excel at factual adaptation, their ability to internalize nuanced philosophical frameworks under severe data constraints remains underexplored. We investigate this by specializing small LLMs on micro-datasets of foundational Stoic texts using preference optimization (ORPO, AlphaPO). Evaluated via a multi-model critic bank, our results show that just 300 high-fidelity examples can induce strong alignment with inward-facing Stoic virtues, closely approaching few-shot prompting while freeing the context window. Critically, however, all models, including few-shot baselines, exhibit a persistent failure on Stoicism's outward-facing cosmopolitan duties, pointing to a representational limitation of small models that micro-dataset adaptation alone cannot overcome.
title StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models
topic Computation and Language
url https://arxiv.org/abs/2605.11483