Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

• URL: http://arxiv.org/abs/2407.01406v2
• Date: Tue, 23 Jul 2024 15:51:12 GMT
• Title: Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
• Authors: Daniil Gurgurov, Mareike Hartmann, Simon Ostermann,
• Abstract summary: This paper explores integration of graph knowledge from linguistic into multilingual Large Models (LLMs) We employ language-specific adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER) We assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER.
• Score: 3.7273829129985305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the integration of graph knowledge from linguistic ontologies into multilingual Large Language Models (LLMs) using adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER). Building upon successful parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, we propose a similar approach for incorporating knowledge from multilingual graphs, connecting concepts in various languages with each other through linguistic relationships, into multilingual LLMs for LRLs. Specifically, we focus on eight LRLs -- Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala -- and employ language-specific adapters fine-tuned on data extracted from the language-specific section of ConceptNet, aiming to enable knowledge transfer across the languages covered by the knowledge graph. We compare various fine-tuning objectives, including standard Masked Language Modeling (MLM), MLM with full-word masking, and MLM with targeted masking, to analyse their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, we assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER, providing insights into the potential benefits of adapting language models for low-resource scenarios.

Related papers

• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Teaching LLMs to Abstain across Languages via Multilingual Feedback [40.84205285309612]
  We show that multilingual feedback helps identify knowledge gaps across diverse languages, cultures, and communities. Extensive experiments demonstrate that our multilingual feedback approach outperforms various strong baselines. Further analysis reveals that multilingual feedback is both an effective and a more equitable abstain strategy to serve diverse language speakers.
  arXiv  Detail & Related papers  (2024-06-22T21:59:12Z)
• Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages [60.162717568496355]
  Large language models (LLMs) have been pre-trained on multilingual corpora. Their performance still lags behind in most languages compared to a few resource-rich languages.
  arXiv  Detail & Related papers  (2024-02-19T15:07:32Z)
• Adapters for Enhanced Modeling of Multilingual Knowledge and Text [54.02078328453149]
  Language models have been extended to multilingual language models (MLLMs) Knowledge graphs contain facts in an explicit triple format, which require careful curation and are only available in a few high-resource languages. We propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages.
  arXiv  Detail & Related papers  (2022-10-24T21:33:42Z)
• Knowledge Based Multilingual Language Model [44.70205282863062]
  We present a novel framework to pretrain knowledge based multilingual language models (KMLMs) We generate a large amount of code-switched synthetic sentences and reasoning-based multilingual training data using the Wikidata knowledge graphs. Based on the intra- and inter-sentence structures of the generated data, we design pretraining tasks to facilitate knowledge learning.
  arXiv  Detail & Related papers  (2021-11-22T02:56:04Z)
• MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning [91.5426763812547]
  Cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. We propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one.
  arXiv  Detail & Related papers  (2021-04-16T06:15:52Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries [8.83363871195679]
  Masked modeling (MLM) objective as key language learning objective. DICT-MLM works by incentivizing the model to be able to predict not just the original masked word, but potentially any of its cross-lingual synonyms as well. Our empirical analysis on multiple downstream tasks spanning 30+ languages, demonstrates the efficacy of the proposed approach.
  arXiv  Detail & Related papers  (2020-10-23T17:53:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.