Summary of Adapter-based Approaches to Knowledge-enhanced Language Models — a Survey, by Alexander Fichtl et al.
Adapter-based Approaches to Knowledge-enhanced Language Models – A Survey
by Alexander Fichtl, Juraj Vladika, Georg Groh
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A systematic literature review is conducted to investigate the use of adapter modules in knowledge-enhanced language models (KELMs). The study provides a structured overview of methodologies, exploring strengths and potential shortcomings. Adapter-based approaches are analyzed for KELMs, focusing on general knowledge, domain-specific approaches, architectures, and tasks. A performance comparison is provided for the biomedical domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Knowledge-enhanced language models can be super helpful tools that make it easier to understand complex information. This study looked at how these models work with adapter modules to learn from data without getting confused. The researchers reviewed lots of previous studies on this topic, found out what works best, and even compared the results for a specific area – medicine. |
