1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
- URL: http://arxiv.org/abs/2406.14721v1
- Date: Thu, 20 Jun 2024 20:32:53 GMT
- Title: 1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
- Authors: Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun,
- Abstract summary: Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages.
Despite their capabilities, they exhibit inconsistencies in handling identical queries in different languages, presenting challenges for further advancement.
This paper introduces a method to enhance the multilingual performance of LLMs by aggregating knowledge from diverse languages.
- Score: 46.43162333819418
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they exhibit inconsistencies in handling identical queries in different languages, presenting challenges for further advancement. This paper introduces a method to enhance the multilingual performance of LLMs by aggregating knowledge from diverse languages. This approach incorporates a low-resource knowledge detector specific to a language, a language selection process, and mechanisms for answer replacement and integration. Our experiments demonstrate notable performance improvements, particularly in reducing language performance disparity. An ablation study confirms that each component of our method significantly contributes to these enhancements. This research highlights the inherent potential of LLMs to harmonize multilingual capabilities and offers valuable insights for further exploration.
Related papers
- 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) - Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
Large Language Models (LLMs) have shown impressive language capabilities.
In this work, we investigate the spontaneous multilingual alignment improvement of LLMs.
We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
arXiv Detail & Related papers (2024-05-22T16:46:19Z) - A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [48.314619377988436]
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing.
Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient.
This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
arXiv Detail & Related papers (2024-05-17T17:47:39Z) - FAC$^2$E: Better Understanding Large Language Model Capabilities by
Dissociating Language and Cognition [57.747888532651]
Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks.
We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
arXiv Detail & Related papers (2024-02-29T21:05:37Z) - Decomposed Prompting: Unveiling Multilingual Linguistic Structure
Knowledge in English-Centric Large Language Models [12.700783525558721]
English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks.
This paper introduces the decomposed prompting approach to probe the linguistic structure understanding of these LLMs in sequence labeling tasks.
arXiv Detail & Related papers (2024-02-28T15:15:39Z) - 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) - Don't Trust ChatGPT when Your Question is not in English: A Study of
Multilingual Abilities and Types of LLMs [16.770697902481107]
Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities.
We propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings.
The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
arXiv Detail & Related papers (2023-05-24T02:05:03Z) - 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)
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.