Vocabulary Expansion for Low-resource Cross-lingual Transfer

Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras·June 17, 2024

Summary

This paper investigates vocabulary expansion for low-resource cross-lingual adaptation of large language models (LLMs) in languages with limited data, focusing on heuristics-based initialization. The study compares different strategies, such as Mean, Merge, and Token Alignment, finding that simpler heuristics are more effective and robust than random or advanced methods in low-resource settings. It covers seven diverse languages, three tasks, and evaluates factors like target vocabulary size, initialization methods, and adaptation data quantity. Heuristics-based initialization outperforms alternatives in 90.5% of cases, highlighting the need for practical adaptation methods for non-English speakers. The research also addresses text overfragmentation and explores vocabulary expansion, which is challenging in low-resource scenarios. While LLaMA2-7B and Mistral-7B models show varying results, the study emphasizes the importance of adapting models to specific languages and scripts for optimal performance.

Key findings

11

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of vocabulary expansion for low-resource cross-lingual transfer in generative Language Learning Models (LLMs) . This problem involves exploring efficient adaptation strategies, including initialization methods, target vocabulary sizes, and adaptation sample sizes, to improve performance in low-resource settings . While vocabulary expansion has been studied before, the paper focuses on heuristics-based initialization methods like Mean and Align, which have shown effectiveness in improving downstream performance and robustness in various languages . This problem is not entirely new but contributes novel insights by emphasizing the importance of sample-efficient adaptation strategies in low-resource scenarios .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the effectiveness of vocabulary expansion in low-resource settings using heuristics-based approaches and the amount of target language data required to achieve comparable or better performance compared to the source and LAPT models . The study investigates the impact of different target vocabulary sizes, the performance changes in zero-shot SPAN with respect to the number of target tokens, and the inference speedups across various languages . Additionally, it explores the adaptation samples and the experiments with other source language models to understand the trends observed in the adaptation process .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Vocabulary Expansion for Low-resource Cross-lingual Transfer" proposes several new ideas, methods, and models related to vocabulary expansion and adaptation in low-resource settings for language models . Here are some key points from the paper:

  1. Heuristics-based Initialization Methods: The paper investigates heuristics-based initialization methods for sample-efficient adaptation that do not rely on external data or models. These methods aim to improve adaptation in low-resource settings without the need for sophisticated techniques or large amounts of data .

  2. Vocabulary Expansion Techniques: The paper introduces vocabulary expansion techniques such as initializing new tokens with the average of corresponding source tokens, using merge rules from the target tokenizer, and aligning new tokens with counterpart tokens in the source vocabulary. These techniques aim to enhance the representation of new tokens and improve model performance .

  3. Token Alignment for Meaning Representation: Token alignment initialization is proposed to ensure that new tokens in the expanded vocabulary have close vector representations with their counterpart tokens in the source vocabulary. This alignment helps maintain the same meaning representation before and after vocabulary expansion .

  4. Optimal Target Vocabulary Size: The paper explores the impact of different target vocabulary sizes on task performance and inference speedups. It recommends setting the new target vocabulary size to around 100 to 500 tokens to maintain competitive performance in low-resource settings .

  5. Adaptation Samples and Training Data: The study analyzes the amount of target language data required to achieve comparable or better performance than the source model. It suggests that models need at least a certain amount of training data to achieve competitive performance with the source model and other adaptation methods .

  6. Future Work and Recommendations: The paper highlights the implications of low-resource settings, the challenges that remain, and provides recommendations for setting target vocabulary sizes, training data amounts, and adaptation methods. It also suggests exploring the efficacy of synthetic and artificial data for cross-lingual transfer of language models in extremely low-resource languages .

Overall, the paper presents innovative approaches to vocabulary expansion and adaptation in low-resource settings, aiming to improve the performance of language models across different languages and tasks. The paper "Vocabulary Expansion for Low-resource Cross-lingual Transfer" introduces novel characteristics and advantages compared to previous methods in the context of vocabulary expansion and adaptation for language models in low-resource settings. Here are the key points based on the details provided in the paper:

  1. Heuristics-based Initialization Methods: The paper proposes heuristics-based initialization methods for sample-efficient adaptation that do not rely on external data or models, distinguishing them from more sophisticated methods that require auxiliary embeddings pre-trained in the target language. These heuristics-based approaches demonstrate better performance and robustness to changes in target vocabulary and adaptation data sizes, particularly when compared to popular methods like random embedding initialization .

  2. Vocabulary Expansion Techniques: The paper introduces innovative vocabulary expansion techniques such as mean initialization and token alignment. Mean initialization involves initializing new tokens with the average of corresponding source tokens, while token alignment ensures that new tokens in the expanded vocabulary have close vector representations with their counterpart tokens in the source vocabulary. These techniques aim to enhance the representation of new tokens and maintain the same meaning representation before and after vocabulary expansion .

  3. Optimal Target Vocabulary Size: The study explores the impact of different target vocabulary sizes on task performance and inference speedups. It recommends setting the new target vocabulary size to around 100 to 500 tokens to maintain competitive performance in low-resource settings. Larger target vocabulary sizes, especially above 1K, tend to result in worse performance, highlighting the importance of an optimal vocabulary size for effective adaptation .

  4. Performance and Robustness: The heuristics-based methods, particularly Mean and Align, show comparable or better performance than the baselines without vocabulary expansion in the majority of cases in low-resource settings. These methods outperform popular approaches like Random and FOCUS, demonstrating their effectiveness in improving downstream performance and robustness to changes in target vocabulary and adaptation data sizes. The heuristics-based methods exhibit competitive results with Source and LAPT, showcasing their efficacy in enhancing language model adaptation .

  5. Future Work and Recommendations: The paper suggests future work to explore the efficacy of synthetic and artificial data for cross-lingual transfer of language models in extremely low-resource languages. It also emphasizes the importance of considering factors like target language overlap with pre-training data, language script, and target task selection when choosing target vocabulary and adaptation sample sizes. These recommendations aim to further enhance the performance and applicability of vocabulary expansion methods in low-resource settings .

In summary, the characteristics and advantages of the proposed heuristics-based vocabulary expansion methods lie in their robustness, improved performance, and suitability for low-resource settings compared to previous methods. These approaches offer a promising avenue for enhancing language model adaptation across diverse languages and tasks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of vocabulary expansion for low-resource cross-lingual transfer. Noteworthy researchers in this area include Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras, Kazuki Fujii, Taishi Nakamura, and many others . The key to the solution mentioned in the paper involves investigating sample-efficient adaptation strategies from different angles, including target vocabulary size, initialization methods, and the amount of target data available for adaptation. The study found that simpler heuristic-based embedding initialization is more efficient and robust in low-resource settings, outperforming more sophisticated approaches that rely on external data and models .


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate the efficacy of vocabulary expansion-based adaptation of generative LLMs under low-resource settings . The study explored sample-efficient adaptation strategies, focusing on initialization methods, target vocabulary, and adaptation sample sizes . The experiments involved extensive testing in seven diverse languages, including Arabic, Greek, Hindi, Japanese, Swahili, and Thai . The study aimed to compare the performance of different adaptation approaches, such as Random, Mean, and Align, in terms of downstream performance and robustness to changes in target vocabulary and adaptation data sizes . The experiments also evaluated the inference speedups achieved by setting different target vocabulary sizes, such as |Vnew| = {50, 100, 500, 1K, 5K, 10K} . Additionally, the study examined the impact of the amount of target language data D on the performance of adapted models compared to baselines like Source and LAPT .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is JNLI, XQuAD, JSQuAD, KenSwQuAD, XL-Sum, and MLSUM . The code for the study is open source and available on GitHub at the following link: https://github.com/gucci-j/lowres-cva .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study extensively explored the efficacy of vocabulary expansion-based adaptation of generative Language Model Models (LLMs) in low-resource settings across seven diverse languages . The experiments investigated sample-efficient adaptation strategies, including initialization methods, target vocabulary sizes, and adaptation sample sizes, demonstrating the effectiveness of heuristics-based methods like Mean and Align over Random in terms of downstream performance and robustness to changes in target vocabulary and data sizes . These findings indicate that the heuristics-based approaches are more likely to be robust in low-resource settings, emphasizing the importance of the adaptation strategies employed in improving model performance .

Furthermore, the results showed that adapted Mistral-7B models using heuristics-based initialization generally outperformed other models like LLaMA2-7B and Random, although they were not always competitive with the baselines without vocabulary expansion . This suggests that different base models may have varying requirements in terms of target vocabulary and data sizes, highlighting the need for tailored approaches based on the specific characteristics of the base models and languages being studied . The study also identified potential task- and language-specific phenomena that could impact vocabulary expansion-based adaptation, indicating avenues for future research to address these challenges .

Overall, the experiments conducted in the paper, along with the detailed analysis of results across different languages and adaptation strategies, provide substantial evidence to support the scientific hypotheses under investigation. The findings offer valuable insights into the effectiveness of vocabulary expansion in low-resource settings and the importance of selecting appropriate adaptation strategies to enhance model performance .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Studying transferable knowledge in language models through pretraining with artificial language .
  • Introducing Mistral 7B, a research work by a group of authors .
  • Presenting datasets as a community library for natural language processing .
  • Discussing the impact of tokenization on language models, specifically analyzing it for Turkish .
  • Introducing KenSwQuAD, a question-answering dataset for Swahili low-resource language .

What work can be continued in depth?

To further advance the research in the field of low-resource cross-lingual transfer, several areas can be explored in depth based on the existing work :

  • Exploration of Different Tokenizers: Investigating the performance of various tokenizers, such as Unigram, beyond the common BPE-based tokenizer used in recent LLMs, could provide insights into their impact on vocabulary expansion and adaptation methods.
  • Expansion to More Languages: While the current work covers seven diverse languages, future research could expand to include a wider range of languages to enhance the generalizability of vocabulary expansion techniques.
  • Investigation of Larger Model Sizes: Conducting experiments with larger LLMs could offer valuable insights into the performance of vocabulary expansion methods with different model sizes and their implications on inference efficiency.
  • Efficiency of Vocabulary Expansion: Further studies can delve into the efficiency and robustness of vocabulary expansion methods, especially in low-resource settings, to optimize adaptation strategies for different target vocabulary sizes and amounts of available data.
  • Synthetic and Artificial Data Exploration: Exploring the efficacy of synthetic and artificial data for cross-lingual transfer of LLMs, particularly in extremely low-resource language scenarios, could provide innovative solutions for vocabulary adaptation and model performance enhancement.

Tables

3

Introduction
Background
Limited data availability in low-resource languages
Importance of cross-lingual adaptation for non-English speakers
Objective
To evaluate heuristics-based initialization for vocabulary expansion
Compare different strategies in low-resource scenarios
Method
Data Collection
Selection of seven diverse languages
Three tasks for evaluation
Limited adaptation data quantity
Data Preprocessing
Text overfragmentation challenges
Vocabulary expansion techniques for low-resource settings
Heuristics-Based Initialization Strategies
Mean Initialization
Description and comparison
Merge Initialization
Approach and evaluation
Token Alignment
Methodology and performance
Random Initialization
Baseline comparison
Advanced Methods
Inefficiency in low-resource conditions
Experiments and Results
Performance Analysis
Target vocabulary size impact
Adaptation effectiveness across languages
Heuristics vs. advanced methods (90.5% success rate)
Case Studies
LLaMA2-7B and Mistral-7B model variations
Language and script adaptation significance
Discussion
Practicality of heuristics for low-resource adaptation
Limitations and future directions
Conclusion
Simplified heuristics as a robust solution
Recommendations for non-English language model adaptation
Importance of addressing overfragmentation in low-resource settings
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
In what percentage of cases does heuristics-based initialization outperform alternative methods?
What challenge does the research address in low-resource scenarios, specifically related to vocabulary expansion?
What is the primary focus of the paper?
Which strategies does the study compare for vocabulary expansion in low-resource cross-lingual adaptation?

Vocabulary Expansion for Low-resource Cross-lingual Transfer

Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras·June 17, 2024

Summary

This paper investigates vocabulary expansion for low-resource cross-lingual adaptation of large language models (LLMs) in languages with limited data, focusing on heuristics-based initialization. The study compares different strategies, such as Mean, Merge, and Token Alignment, finding that simpler heuristics are more effective and robust than random or advanced methods in low-resource settings. It covers seven diverse languages, three tasks, and evaluates factors like target vocabulary size, initialization methods, and adaptation data quantity. Heuristics-based initialization outperforms alternatives in 90.5% of cases, highlighting the need for practical adaptation methods for non-English speakers. The research also addresses text overfragmentation and explores vocabulary expansion, which is challenging in low-resource scenarios. While LLaMA2-7B and Mistral-7B models show varying results, the study emphasizes the importance of adapting models to specific languages and scripts for optimal performance.
Mind map
Inefficiency in low-resource conditions
Advanced Methods
Baseline comparison
Random Initialization
Methodology and performance
Token Alignment
Approach and evaluation
Merge Initialization
Description and comparison
Mean Initialization
Language and script adaptation significance
LLaMA2-7B and Mistral-7B model variations
Heuristics vs. advanced methods (90.5% success rate)
Adaptation effectiveness across languages
Target vocabulary size impact
Heuristics-Based Initialization Strategies
Limited adaptation data quantity
Three tasks for evaluation
Selection of seven diverse languages
Compare different strategies in low-resource scenarios
To evaluate heuristics-based initialization for vocabulary expansion
Importance of cross-lingual adaptation for non-English speakers
Limited data availability in low-resource languages
Importance of addressing overfragmentation in low-resource settings
Recommendations for non-English language model adaptation
Simplified heuristics as a robust solution
Limitations and future directions
Practicality of heuristics for low-resource adaptation
Case Studies
Performance Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Limited data availability in low-resource languages
Importance of cross-lingual adaptation for non-English speakers
Objective
To evaluate heuristics-based initialization for vocabulary expansion
Compare different strategies in low-resource scenarios
Method
Data Collection
Selection of seven diverse languages
Three tasks for evaluation
Limited adaptation data quantity
Data Preprocessing
Text overfragmentation challenges
Vocabulary expansion techniques for low-resource settings
Heuristics-Based Initialization Strategies
Mean Initialization
Description and comparison
Merge Initialization
Approach and evaluation
Token Alignment
Methodology and performance
Random Initialization
Baseline comparison
Advanced Methods
Inefficiency in low-resource conditions
Experiments and Results
Performance Analysis
Target vocabulary size impact
Adaptation effectiveness across languages
Heuristics vs. advanced methods (90.5% success rate)
Case Studies
LLaMA2-7B and Mistral-7B model variations
Language and script adaptation significance
Discussion
Practicality of heuristics for low-resource adaptation
Limitations and future directions
Conclusion
Simplified heuristics as a robust solution
Recommendations for non-English language model adaptation
Importance of addressing overfragmentation in low-resource settings
Key findings
11

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of vocabulary expansion for low-resource cross-lingual transfer in generative Language Learning Models (LLMs) . This problem involves exploring efficient adaptation strategies, including initialization methods, target vocabulary sizes, and adaptation sample sizes, to improve performance in low-resource settings . While vocabulary expansion has been studied before, the paper focuses on heuristics-based initialization methods like Mean and Align, which have shown effectiveness in improving downstream performance and robustness in various languages . This problem is not entirely new but contributes novel insights by emphasizing the importance of sample-efficient adaptation strategies in low-resource scenarios .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the effectiveness of vocabulary expansion in low-resource settings using heuristics-based approaches and the amount of target language data required to achieve comparable or better performance compared to the source and LAPT models . The study investigates the impact of different target vocabulary sizes, the performance changes in zero-shot SPAN with respect to the number of target tokens, and the inference speedups across various languages . Additionally, it explores the adaptation samples and the experiments with other source language models to understand the trends observed in the adaptation process .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Vocabulary Expansion for Low-resource Cross-lingual Transfer" proposes several new ideas, methods, and models related to vocabulary expansion and adaptation in low-resource settings for language models . Here are some key points from the paper:

  1. Heuristics-based Initialization Methods: The paper investigates heuristics-based initialization methods for sample-efficient adaptation that do not rely on external data or models. These methods aim to improve adaptation in low-resource settings without the need for sophisticated techniques or large amounts of data .

  2. Vocabulary Expansion Techniques: The paper introduces vocabulary expansion techniques such as initializing new tokens with the average of corresponding source tokens, using merge rules from the target tokenizer, and aligning new tokens with counterpart tokens in the source vocabulary. These techniques aim to enhance the representation of new tokens and improve model performance .

  3. Token Alignment for Meaning Representation: Token alignment initialization is proposed to ensure that new tokens in the expanded vocabulary have close vector representations with their counterpart tokens in the source vocabulary. This alignment helps maintain the same meaning representation before and after vocabulary expansion .

  4. Optimal Target Vocabulary Size: The paper explores the impact of different target vocabulary sizes on task performance and inference speedups. It recommends setting the new target vocabulary size to around 100 to 500 tokens to maintain competitive performance in low-resource settings .

  5. Adaptation Samples and Training Data: The study analyzes the amount of target language data required to achieve comparable or better performance than the source model. It suggests that models need at least a certain amount of training data to achieve competitive performance with the source model and other adaptation methods .

  6. Future Work and Recommendations: The paper highlights the implications of low-resource settings, the challenges that remain, and provides recommendations for setting target vocabulary sizes, training data amounts, and adaptation methods. It also suggests exploring the efficacy of synthetic and artificial data for cross-lingual transfer of language models in extremely low-resource languages .

Overall, the paper presents innovative approaches to vocabulary expansion and adaptation in low-resource settings, aiming to improve the performance of language models across different languages and tasks. The paper "Vocabulary Expansion for Low-resource Cross-lingual Transfer" introduces novel characteristics and advantages compared to previous methods in the context of vocabulary expansion and adaptation for language models in low-resource settings. Here are the key points based on the details provided in the paper:

  1. Heuristics-based Initialization Methods: The paper proposes heuristics-based initialization methods for sample-efficient adaptation that do not rely on external data or models, distinguishing them from more sophisticated methods that require auxiliary embeddings pre-trained in the target language. These heuristics-based approaches demonstrate better performance and robustness to changes in target vocabulary and adaptation data sizes, particularly when compared to popular methods like random embedding initialization .

  2. Vocabulary Expansion Techniques: The paper introduces innovative vocabulary expansion techniques such as mean initialization and token alignment. Mean initialization involves initializing new tokens with the average of corresponding source tokens, while token alignment ensures that new tokens in the expanded vocabulary have close vector representations with their counterpart tokens in the source vocabulary. These techniques aim to enhance the representation of new tokens and maintain the same meaning representation before and after vocabulary expansion .

  3. Optimal Target Vocabulary Size: The study explores the impact of different target vocabulary sizes on task performance and inference speedups. It recommends setting the new target vocabulary size to around 100 to 500 tokens to maintain competitive performance in low-resource settings. Larger target vocabulary sizes, especially above 1K, tend to result in worse performance, highlighting the importance of an optimal vocabulary size for effective adaptation .

  4. Performance and Robustness: The heuristics-based methods, particularly Mean and Align, show comparable or better performance than the baselines without vocabulary expansion in the majority of cases in low-resource settings. These methods outperform popular approaches like Random and FOCUS, demonstrating their effectiveness in improving downstream performance and robustness to changes in target vocabulary and adaptation data sizes. The heuristics-based methods exhibit competitive results with Source and LAPT, showcasing their efficacy in enhancing language model adaptation .

  5. Future Work and Recommendations: The paper suggests future work to explore the efficacy of synthetic and artificial data for cross-lingual transfer of language models in extremely low-resource languages. It also emphasizes the importance of considering factors like target language overlap with pre-training data, language script, and target task selection when choosing target vocabulary and adaptation sample sizes. These recommendations aim to further enhance the performance and applicability of vocabulary expansion methods in low-resource settings .

In summary, the characteristics and advantages of the proposed heuristics-based vocabulary expansion methods lie in their robustness, improved performance, and suitability for low-resource settings compared to previous methods. These approaches offer a promising avenue for enhancing language model adaptation across diverse languages and tasks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of vocabulary expansion for low-resource cross-lingual transfer. Noteworthy researchers in this area include Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras, Kazuki Fujii, Taishi Nakamura, and many others . The key to the solution mentioned in the paper involves investigating sample-efficient adaptation strategies from different angles, including target vocabulary size, initialization methods, and the amount of target data available for adaptation. The study found that simpler heuristic-based embedding initialization is more efficient and robust in low-resource settings, outperforming more sophisticated approaches that rely on external data and models .


How were the experiments in the paper designed?

The experiments in the paper were designed to investigate the efficacy of vocabulary expansion-based adaptation of generative LLMs under low-resource settings . The study explored sample-efficient adaptation strategies, focusing on initialization methods, target vocabulary, and adaptation sample sizes . The experiments involved extensive testing in seven diverse languages, including Arabic, Greek, Hindi, Japanese, Swahili, and Thai . The study aimed to compare the performance of different adaptation approaches, such as Random, Mean, and Align, in terms of downstream performance and robustness to changes in target vocabulary and adaptation data sizes . The experiments also evaluated the inference speedups achieved by setting different target vocabulary sizes, such as |Vnew| = {50, 100, 500, 1K, 5K, 10K} . Additionally, the study examined the impact of the amount of target language data D on the performance of adapted models compared to baselines like Source and LAPT .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is JNLI, XQuAD, JSQuAD, KenSwQuAD, XL-Sum, and MLSUM . The code for the study is open source and available on GitHub at the following link: https://github.com/gucci-j/lowres-cva .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study extensively explored the efficacy of vocabulary expansion-based adaptation of generative Language Model Models (LLMs) in low-resource settings across seven diverse languages . The experiments investigated sample-efficient adaptation strategies, including initialization methods, target vocabulary sizes, and adaptation sample sizes, demonstrating the effectiveness of heuristics-based methods like Mean and Align over Random in terms of downstream performance and robustness to changes in target vocabulary and data sizes . These findings indicate that the heuristics-based approaches are more likely to be robust in low-resource settings, emphasizing the importance of the adaptation strategies employed in improving model performance .

Furthermore, the results showed that adapted Mistral-7B models using heuristics-based initialization generally outperformed other models like LLaMA2-7B and Random, although they were not always competitive with the baselines without vocabulary expansion . This suggests that different base models may have varying requirements in terms of target vocabulary and data sizes, highlighting the need for tailored approaches based on the specific characteristics of the base models and languages being studied . The study also identified potential task- and language-specific phenomena that could impact vocabulary expansion-based adaptation, indicating avenues for future research to address these challenges .

Overall, the experiments conducted in the paper, along with the detailed analysis of results across different languages and adaptation strategies, provide substantial evidence to support the scientific hypotheses under investigation. The findings offer valuable insights into the effectiveness of vocabulary expansion in low-resource settings and the importance of selecting appropriate adaptation strategies to enhance model performance .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Studying transferable knowledge in language models through pretraining with artificial language .
  • Introducing Mistral 7B, a research work by a group of authors .
  • Presenting datasets as a community library for natural language processing .
  • Discussing the impact of tokenization on language models, specifically analyzing it for Turkish .
  • Introducing KenSwQuAD, a question-answering dataset for Swahili low-resource language .

What work can be continued in depth?

To further advance the research in the field of low-resource cross-lingual transfer, several areas can be explored in depth based on the existing work :

  • Exploration of Different Tokenizers: Investigating the performance of various tokenizers, such as Unigram, beyond the common BPE-based tokenizer used in recent LLMs, could provide insights into their impact on vocabulary expansion and adaptation methods.
  • Expansion to More Languages: While the current work covers seven diverse languages, future research could expand to include a wider range of languages to enhance the generalizability of vocabulary expansion techniques.
  • Investigation of Larger Model Sizes: Conducting experiments with larger LLMs could offer valuable insights into the performance of vocabulary expansion methods with different model sizes and their implications on inference efficiency.
  • Efficiency of Vocabulary Expansion: Further studies can delve into the efficiency and robustness of vocabulary expansion methods, especially in low-resource settings, to optimize adaptation strategies for different target vocabulary sizes and amounts of available data.
  • Synthetic and Artificial Data Exploration: Exploring the efficacy of synthetic and artificial data for cross-lingual transfer of LLMs, particularly in extremely low-resource language scenarios, could provide innovative solutions for vocabulary adaptation and model performance enhancement.
Tables
3
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