mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models

• URL: http://arxiv.org/abs/2305.13684v3
• Date: Fri, 5 Jul 2024 17:19:52 GMT
• Title: mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models
• Authors: Peiqin Lin, Chengzhi Hu, Zheyu Zhang, André F. T. Martins, Hinrich Schütze,
• Abstract summary: We propose mPLMSim, a language similarity measure that induces the similarities across languages from mPLMs using multi-parallel corpora. Our study shows that mPLM-Sim exhibits moderately high correlations with linguistic similarity measures, such as lexico, genealogical language family, and geographical sprachbund. We further investigate whether mPLMSim is effective for zero-shot cross-lingual transfer by conducting experiments on both low-level syntactic tasks and high-level semantic tasks.
• Score: 57.225289079198454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent multilingual pretrained language models (mPLMs) have been shown to encode strong language-specific signals, which are not explicitly provided during pretraining. It remains an open question whether it is feasible to employ mPLMs to measure language similarity, and subsequently use the similarity results to select source languages for boosting cross-lingual transfer. To investigate this, we propose mPLMSim, a language similarity measure that induces the similarities across languages from mPLMs using multi-parallel corpora. Our study shows that mPLM-Sim exhibits moderately high correlations with linguistic similarity measures, such as lexicostatistics, genealogical language family, and geographical sprachbund. We also conduct a case study on languages with low correlation and observe that mPLM-Sim yields more accurate similarity results. Additionally, we find that similarity results vary across different mPLMs and different layers within an mPLM. We further investigate whether mPLMSim is effective for zero-shot cross-lingual transfer by conducting experiments on both low-level syntactic tasks and high-level semantic tasks. The experimental results demonstrate that mPLM-Sim is capable of selecting better source languages than linguistic measures, resulting in a 1%-2% improvement in zero-shot cross-lingual transfer performance.

Related papers

• Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models [104.96990850774566]
  We propose a Multi-lingual Ability Extraction and Transfer approach, named as MAET. Our key idea is to decompose and extract language-agnostic ability-related weights from large language models. Experiment results show MAET can effectively and efficiently extract and transfer the advanced abilities, and outperform training-based baseline methods.
  arXiv  Detail & Related papers  (2024-10-10T11:23:18Z)
• Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis [19.37853222555255]
  Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages.
  arXiv  Detail & Related papers  (2024-09-22T14:14:05Z)
• In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation [20.704153242284114]
  We focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. No systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection. We find that sentence embedding similarity can improve MT, especially for low-resource language directions.
  arXiv  Detail & Related papers  (2024-08-01T09:07:32Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Quantifying Multilingual Performance of Large Language Models Across Languages [48.40607157158246]
  Large Language Models (LLMs) perform better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. We propose the Language Ranker, an intrinsic metric designed to benchmark and rank languages based on LLM performance using internal representations. Our analysis reveals that high-resource languages exhibit higher similarity scores with English, demonstrating superior performance, while low-resource languages show lower similarity scores.
  arXiv  Detail & Related papers  (2024-04-17T16:53:16Z)
• Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation [25.850573463743352]
  Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive performance on cross-language tasks. Yet significant performance disparities exist across different languages within the same mPLM. We introduce ALSACE to leverage the learned knowledge from the well-performing languages to guide under-performing ones within the same mPLM.
  arXiv  Detail & Related papers  (2024-04-12T14:19:16Z)
• Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets [4.653113033432781]
  Cross-lingual transfer capabilities of Multilingual Language Models (MLLMs) are investigated. Our research provides valuable insights into cross-lingual transfer and its implications for NLP applications.
  arXiv  Detail & Related papers  (2024-03-29T08:47:15Z)
• Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
  Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs. Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
  arXiv  Detail & Related papers  (2024-02-26T09:36:05Z)
• Extrapolating Large Language Models to Non-English by Aligning Languages [109.09051737966178]
  Existing large language models show disparate capability across different languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages.
  arXiv  Detail & Related papers  (2023-08-09T13:32:06Z)
• How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning [14.02101305717738]
  Multilingual large language models (MLLMs) are jointly trained on data from many different languages. It remains unclear to what extent, and under which conditions, languages rely on each other's data. We find that MLLMs rely on data from multiple languages from the early stages of fine-tuning and that this reliance gradually increases as fine-tuning progresses.
  arXiv  Detail & Related papers  (2023-05-22T17:47:41Z)

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.