Summary of Adapting Multilingual Llms to Low-resource Languages with Knowledge Graphs Via Adapters, by Daniil Gurgurov et al.
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
by Daniil Gurgurov, Mareike Hartmann, Simon Ostermann
First submitted to arxiv on: 1 Jul 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 This paper proposes a novel approach to improving the performance of Large Language Models (LLMs) for low-resource languages (LRLs) by integrating graph knowledge from linguistic ontologies using adapters. The method builds upon existing parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, to incorporate knowledge from multilingual graphs into LLMs for LRLs. Specifically, the paper focuses on eight LRLs: Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala, and employs language-specific adapters fine-tuned on data extracted from ConceptNet. The authors compare various fine-tuning objectives to analyze their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, the paper assesses how structured graph knowledge affects the performance of multilingual LLMs for LRLs in sentiment analysis (SA) and named entity recognition (NER), providing insights into the potential benefits of adapting language models for low-resource scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving computer language models for languages that don’t have much data. They want to make these models better by adding knowledge from other languages that have more information. The authors use a special technique called adapters to mix this knowledge with the existing model. They tested this method on eight different languages and found it improved the model’s ability to understand sentences and find important words. This could help people in countries where language resources are limited, by giving them better tools for understanding and generating text. |
Keywords
» Artificial intelligence » Fine tuning » Named entity recognition » Ner » Parameter efficient
