综述适配器增强型语言模型如何提升事实准确性与减少幻觉。
Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey
- 用适配器模块降低知识增强模型的计算负担和遗忘风险。
- 发现通用知识与领域特定方法在不同任务中各有优势。
- 重点分析生物医学领域模型表现，适合医疗文本研究者参考。
知识增强型语言模型（KELMs）已成为弥合大规模语言模型与领域知识之间差距的有力工具。通过利用知识图谱（KGs），KELMs 能够提升事实准确性并缓解幻觉问题。为减轻计算开销与灾难性遗忘风险，常将适配器模块集成到模型中。本文对基于适配器的 KELM 方法进行了系统文献回顾（SLR），通过定量与定性分析梳理了现有方法的结构，并探讨各方法的优势与潜在不足。研究表明，通用知识与领域特定方法均被广泛研究，适配器架构及下游任务多样。尤其聚焦生物医学领域，提供了现有 KELMs 的性能对比。文章总结主要趋势，并提出未来有潜力的研究方向。
原文摘要 · Abstract (English)
Knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large-scale language models and domain-specific knowledge. KELMs can achieve higher factual accuracy and mitigate hallucinations by leveraging knowledge graphs (KGs). They are frequently combined with adapter modules to reduce the computational load and risk of catastrophic forgetting. In this paper, we conduct a systematic literature review (SLR) on adapter-based approaches to KELMs. We provide a structured overview of existing methodologies in the field through quantitative and qualitative analysis and explore the strengths and potential shortcomings of individual approaches. We show that general knowledge and domain-specific approaches have been frequently explored along with various adapter architectures and downstream tasks. We particularly focused on the popular biomedical domain, where we provided an insightful performance comparison of existing KELMs. We outline the main trends and propose promising future directions.
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