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About this paper
Alexander Fichtl, Juraj Vladika, Georg Groh
This paper provides a comprehensive survey of adapter-based approaches for knowledge enhancement in large language models, analyzing current methodologies and trends. It identifies how these methods efficiently integrate external knowledge, particularly in domains like biomedicine, to improve factual accuracy while significantly reducing computational overhead compared to full fine-tuning.
Key insights
- Adapter-based KELMs provide a parameter-efficient alternative to full fine-tuning for integrating external knowledge into large language models.
- Pfeiffer and Houlsby adapter architectures are predominantly used, reflecting their established effectiveness and availability in research.
- The biomedical domain stands out as a primary application area for adapter-based knowledge enhancement due to the critical need for factual accuracy and the availability of specialized knowledge graphs.
Summary by alphaXiv.
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