The paper aims to investigate the impact of using native and translated data during instruction tuning and evaluation for large language models (LLMs) . It specifically addresses the question of whether there is a performance gap between using translated and native data, especially when the model performance is strong, and explores techniques to bridge this gap . The study also delves into the effectiveness of training regularization, like lower learning rates or multilingual instruction tuning, in closing the performance gap between models trained on native and translated data . This research question is not entirely new, but the paper contributes by systematically studying the impact of native and translated data on LLM performance across different benchmarks and model sizes .

This paper aims to validate the scientific hypothesis related to the impact of native and translated data on instruction tuning and evaluation for large language models. The research question focuses on whether there is a performance gap between native and translated data, especially when the model performance is strong, and what techniques can be employed to bridge this gap . The study systematically investigates the influence of native and translated data on model performance across different benchmarks and model sizes, highlighting the importance of evaluating data factors carefully to make informed decisions . The paper also explores the effectiveness of training regularization techniques like lower learning rates and multilingual instruction tuning in closing the performance gap between models trained on native and translated data, particularly on structured tasks .

The paper "Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?" proposes several new ideas, methods, and models related to instruction tuning and evaluation of large language models (LLMs) . Here are some key points from the paper:

1. Investigation of Native and Translated Data: The paper systematically investigates the impact of native and translated data during instruction tuning and evaluation of LLMs. It explores how the use of native and translated data can lead to performance gaps, especially when the model performance is strong .

2. Performance Gap on Different Benchmarks: The research findings suggest that the performance gap between native and translated data is more pronounced on benchmarks that are natively created or generative in nature. This difference is backed by correlation analysis .

3. Training Regularization Techniques: The paper discusses the benefits of training regularization techniques, such as using a lower learning rate or multilingual instruction tuning, to bridge the performance gap between models instruction-tuned on native data and translated data. These techniques are shown to be beneficial for structured tasks but not as effective for generative tasks .

4. Multilingual Instruction Tuning: The study explores multilingual instruction tuning as a method to prevent models from overfitting to a single language. It evaluates model performance in languages like Spanish, Russian, Chinese, Arabic, German, Finnish, Irish, and Hindi. The paper recommends evaluating multilingual LLMs on a range of benchmarks, including language-native or generative tasks .

5. Experimental Setup and Results: The paper details the technical setup, base models used, and the experimental design for instruction tuning and evaluation. It presents results from experiments with different base models, instruction tuning approaches, and data variations across various benchmarks like TyDi QA, CMMLU, XQuAD, and open-ended question answering .

Overall, the paper introduces insights into the impact of native and translated data on LLM performance, proposes training regularization techniques, and advocates for multilingual instruction tuning to enhance the evaluation of large language models across different languages and benchmarks . The paper "Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?" introduces novel characteristics and advantages compared to previous methods in the field of instruction tuning and evaluation of large language models (LLMs) .

1. Native and Translated Data Investigation: The paper systematically investigates the impact of native and translated data during instruction tuning and evaluation of LLMs. It highlights that a prudent choice in evaluation options is crucial, especially when evaluating data factors, to bridge the performance gap between native and translated data .

2. Performance Gap Analysis: The research findings reveal that native and translated data can lead to a performance gap, particularly on benchmarks that are natively created or generative in nature. This difference is supported by correlation analysis, emphasizing the importance of understanding the impact of data nature on model performance .

3. Training Regularization Techniques: The paper discusses the benefits of training regularization techniques, such as using a lower learning rate or multilingual instruction tuning, to address the performance gap between models instruction-tuned on native and translated data. These techniques are shown to be effective for structured tasks but less so for generative tasks .

4. Multilingual Instruction Tuning: The study advocates for multilingual instruction tuning as a method to prevent models from overfitting to a single language. It recommends evaluating multilingual LLMs on a variety of benchmarks, including language-native or generative tasks, to enhance the evaluation process across different languages .

5. Experimental Setup and Results: The paper details the technical setup, base models used, and the experimental design for instruction tuning and evaluation. It presents results from experiments with different base models, instruction tuning approaches, and data variations across various benchmarks like TyDi QA, CMMLU, XQuAD, and open-ended question answering, providing comprehensive insights into the performance of LLMs .

Overall, the paper's innovative approach lies in its thorough investigation of native and translated data, the application of training regularization techniques, and the recommendation of multilingual instruction tuning to enhance the evaluation of large language models across multiple languages and benchmarks, contributing valuable insights to the field of multilingual LLM evaluation .

Several related research works exist in the field of multilingual instruction tuning and evaluation for large language models. Noteworthy researchers in this area include Zhihong Chen, Shuo Yan, Juhao Liang, Feng Jiang, and others . The key to the solution mentioned in the paper involves investigating the impact of native and translated data during instruction tuning and evaluation, experimenting with monolingual instruction tuning in languages like Spanish, Russian, and Chinese, and utilizing training regularization techniques like a lower learning rate or multilingual instruction tuning to bridge the performance gap between models trained on native and translated data .

The experiments in the paper "Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?" were designed to systematically investigate the impact of native and translated data during instruction tuning and evaluation . The study focused on answering three key questions related to the nature of instruction data and its influence on evaluation outcomes . The experiments involved evaluating model performance in three languages - Spanish, Russian, and Chinese - using both native and translated data . The study explored the performance differences between models trained on native data versus translated data, especially when the model performance is strong . Additionally, the experiments included training regularization techniques like lower learning rates or multilingual instruction tuning to bridge the performance gap between models trained on native and translated data . The study recommended that multilingual (non-English) Large Language Model (LLM) evaluation should be conducted on a variety of benchmarks, including language-native or generative tasks .

The dataset used for quantitative evaluation in the study is the TyDi QA dataset, which is a benchmark for information-seeking question answering in typologically diverse languages . The code for the study, specifically the MultilingualSIFT, is open source and available on GitHub .

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study systematically investigates the impact of native and translated data on instruction tuning and evaluation for large language models across different benchmarks . The findings reveal that there can be a performance gap between models trained on native and translated data, especially when the model performance is strong . This performance difference is more pronounced on benchmarks that are natively created or generative in nature, as supported by correlation analysis . Additionally, the study demonstrates that training regularization techniques like lower learning rates or multilingual instruction tuning can help bridge the performance gap, particularly on structured tasks .

Moreover, the research explores multilingual instruction tuning, which aims to prevent models from overfitting to a single language by incorporating multiple languages in the training process . The study evaluates model performance in languages such as Spanish, Russian, Chinese, Arabic, German, Finnish, Irish, and Hindi, covering a diverse set of language families and writing scripts . By experimenting with monolingual instruction tuning and multilingual data sets derived from translating English resources, the study provides a comprehensive analysis of the impact of instruction data nature on model performance .

Overall, the experiments conducted in the paper, along with the detailed analysis of the results, offer valuable insights into the influence of native and translated data on large language model performance, providing substantial support for the scientific hypotheses under investigation . The study's rigorous methodology and empirical findings contribute significantly to the understanding of the factors affecting the performance of multilingual instruction tuning and evaluation for large language models.

The paper investigates the impact of native and translated data on instruction tuning and evaluation for large language models. The key contributions of the paper include:

• Systematically studying native and translated data during instruction tuning and evaluation on various models and benchmarks .
• Highlighting that a prudent choice in evaluation options is crucial, especially when evaluating data factors, and demonstrating the performance gap between native and translated data, particularly on specific benchmarks .
• Showing that training regularization techniques like lower learning rates or multilingual instruction tuning can help bridge the performance gap between models tuned on native and translated data, especially on structured tasks .
• Recommending that multilingual (non-English) large language model evaluation should be conducted across a range of benchmarks, including language-native or generative tasks .

To delve deeper into the research presented in the document, several avenues for further exploration can be pursued:

• Investigating the Impact of Native and Translated Data: Further research can focus on understanding the nuances of how native and translated data influence model performance, especially in scenarios where there is a notable performance gap, particularly on benchmarks that are natively created or generative in nature .
• Exploring Multilingual Instruction Tuning: There is potential for in-depth exploration of multilingual instruction tuning to prevent models from overfitting to a single language. This could involve expanding the evaluation to include additional languages beyond Spanish, Russian, and Chinese, such as Arabic, German, Finnish, Irish, and Hindi, to create a more comprehensive multilingual dataset .
• Enhancing Model Performance: Further investigations could focus on techniques like training regularization with lower learning rates or multilingual instruction tuning to bridge the performance gap between models trained on native and translated data. This could involve experimenting with different learning rates and exploring how they impact model performance on structured and generative tasks .
• Extending Evaluation to Diverse Benchmarks: Future work could involve evaluating models on a wider range of benchmarks, including language-native or generative tasks, to gain a more comprehensive understanding of how multilingual large language models perform across different types of tasks and datasets .