Neuro-Symbolic Prompt Tuning for Trustworthy Reasoning in Large Language Models
Keywords:
neuro-symbolic AI, prompt tuning, large language models, trustworthy reasoning, system governance, fairness, sustainabilityAbstract
Large language models have exhibited remarkable generative capabilities, yet their deployment in high-stakes domains is constrained by persistent concerns regarding trustworthiness, interpretability, and systematic reasoning fidelity. Prompt tuning has emerged as a parameter-efficient adaptation strategy, but conventional approaches typically treat the prompt space as a continuous set of vectors optimized solely for task performance, neglecting the structured constraints that underpin reliable inference. This paper proposes a neuro-symbolic prompt tuning framework that integrates symbolic reasoning modules directly into the prompt optimization pipeline, thereby aligning the continuous adaptation of prompts with logical, rule-based verification and knowledge structures. We provide a system-level examination of this architecture, focusing on structural trade-offs among modular neuro-symbolic components, the governance of hybrid reasoning workflows, and the infrastructure required for sustainable and fair deployment. We analyze how symbolic guardrails, injected at the prompt tuning stage, can mitigate hallucination, improve consistency across diverse demographic contexts, and enable auditable decision pathways. The discussion extends to infrastructure considerations, including compute partitioning, energy-aware scheduling, and the incorporation of human-in-the-loop oversight to ensure that symbolic knowledge bases remain aligned with evolving societal norms. Policy implications of such reasoning-augmented models are examined through the lens of the EU AI Act and emerging fairness regulations, emphasizing the need for transparent documentation of symbolic rule provenance. The paper concludes by outlining a roadmap for transforming prompt tuning from a purely statistical optimization into a sociotechnically grounded engineering discipline, balancing performance, fairness, robustness, and accountability.
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