Graph-Enhanced Prompt Tuning for Knowledge Reasoning and Entity-Centric Language Understanding
Keywords:
prompt tuning, knowledge graphs, graph neural networks, entity-centric reasoning, parameter-efficient adaptation, language model governanceAbstract
The rapid evolution of large language models has transformed natural language processing, yet these models still exhibit notable limitations in knowledge-intensive reasoning and fine-grained entity understanding. Prompt tuning, a parameter-efficient adaptation method, offers a lightweight alternative to full fine-tuning, but conventional prompt-based approaches often lack structured knowledge integration, leading to hallucinations and shallow semantic processing. This paper proposes a graph-enhanced prompt tuning framework that embeds entity-centric knowledge graphs into continuous prompt representations to strengthen reasoning capabilities. We examine the architectural interplay between graph neural networks, knowledge graphs, and prompt-based learning, analyzing system-level trade-offs related to latency, memory footprint, and scalability. The framework integrates a dynamic graph encoding module that captures relational and hierarchical entity information, projecting graph embeddings into the prompt space through an adapter layer. A selective insertion mechanism determines where knowledge prompts are most impactful, enabling context-aware augmentation without overwhelming the language model’s pre-trained priors. We further discuss deployment implications across cloud, edge, and hybrid infrastructures, highlighting the tension between knowledge freshness, inference cost, and maintainability. Robustness and fairness are scrutinized through the lens of graph-induced biases, uneven entity coverage, and adversarial vulnerability in knowledge bases. The governance and policy dimensions of entity-centric reasoning systems are explored, including data provenance, licensing, and accountability in high-stakes domains such as healthcare and finance. By synthesizing architectural design choices with socio-technical considerations, this paper provides a comprehensive examination of graph-enhanced prompt tuning as a pathway toward more reliable, transparent, and entity-aware language understanding systems.
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