Evaluating Zero-shot Cross-lingual Transfer in Instruction Tuning of Large Language Models

Keskeiset käsitteet

Cross-lingual transfer can happen successfully in Instruction Tuning even if all stages of model training are English-centric, but only if multilinguality is taken into account in hyperparameter tuning and with large enough Instruction Tuning data.

Tiivistelmä

The content presents a systematic study of zero-shot cross-lingual transfer in Instruction Tuning (IT), where a large language model (LLM) is instruction-tuned on English-only data and then tested on user prompts in other languages.
The key findings include:

Cross-lingual transfer does happen successfully in IT even if all stages of model training are English-centric, but only if multilinguality is taken into account in hyperparameter tuning and with large enough IT data.
English-trained LLMs are capable of generating correct-language, comprehensive and helpful responses in the other languages, but suffer from low factuality and may occasionally have fluency errors.
Careful hyperparameter tuning, especially of the learning rate, is essential for achieving good multilingual instruction following capabilities.
Using a multilingual base model or multilingual IT data can further improve fluency and generation in the correct language, but does not solve the factuality issue.
Training on a small IT dataset leads to severe overfitting to English and poor cross-lingual performance.
The authors advocate for a more comprehensive evaluation methodology that assesses various aspects of model responses, controls task distribution and complexity, and uses both automatic and human evaluation.

Tilastot

Instruction tuning datasets: Dolly (15k English instructions) and LIMA (1k English instructions)
Multilingual Dolly dataset created by translating English instructions to French, Portuguese, and Russian
Base models: LLaMA-2 (7B and 13B, English-centric) and TowerBase-7B (10 languages)
Adaptation strategies: Full finetuning (FT) and Low-Rank Adaptation (LoRA)

Lainaukset

"Cross-lingual transfer does happen successfully in Instruction Tuning (IT) even if all stages of model training are English-centric, but only if multilinguality is taken into account in IT hyperparameter tuning and with large enough IT data."
"English-trained LLMs are capable of generating correct-language, comprehensive and helpful responses in the other languages, but suffer from low factuality and may occasionally have fluency errors."

Tärkeimmät oivallukset

Zero-shot cross-lingual transfer in instruction tuning of large language models

by Nadezhda Chi... klo arxiv.org 04-23-2024

https://arxiv.org/pdf/2402.14778.pdf

Syvällisempiä Kysymyksiä

How can we further improve the factual accuracy of cross-lingual instruction following in English-centric language models?

To enhance the factual accuracy of cross-lingual instruction following in English-centric language models, several strategies can be implemented:

Fine-tuning on Multilingual Data: Incorporating multilingual data during the fine-tuning process can expose the model to a wider range of linguistic patterns and factual information across languages, thereby improving its overall accuracy in generating responses in different languages.

Fact-Checking Mechanisms: Implementing fact-checking mechanisms during the training phase can help the model verify the accuracy of the information it generates in response to instructions. This can involve integrating external fact-checking APIs or databases to validate the correctness of the generated content.

Domain-Specific Knowledge: Providing the model with domain-specific knowledge and context can improve its ability to generate accurate responses. By training the model on a diverse set of instructions from various domains, it can develop a more comprehensive understanding of different topics and improve its factual accuracy.

Continuous Evaluation and Feedback: Regularly evaluating the model's performance on a diverse set of instructions and incorporating feedback from human evaluators can help identify and correct inaccuracies. This iterative process can lead to continuous improvement in the model's factual accuracy over time.

What other factors, beyond hyperparameters and data size, could influence the success of zero-shot cross-lingual transfer in instruction tuning?

In addition to hyperparameters and data size, several other factors can influence the success of zero-shot cross-lingual transfer in instruction tuning:

Model Architecture: The design and complexity of the model architecture can impact its ability to transfer knowledge across languages. Models with more advanced architectures, such as transformer-based models, may exhibit better cross-lingual transfer capabilities.

Training Regimen: The training regimen, including the duration of training, the quality of the training data, and the optimization techniques used, can significantly impact the model's performance in zero-shot cross-lingual transfer. Adequate training with diverse and representative data is crucial for successful transfer learning.

Language Similarity: The linguistic similarity between the source language (English) and target languages can affect the model's ability to transfer knowledge effectively. Languages that share more linguistic features and structures with English may exhibit better transfer performance.

Fine-Tuning Strategies: The approach taken for fine-tuning the model on the instruction data, such as the adaptation method (e.g., full finetuning vs. low-rank adaptation) and the choice of hyperparameters, can influence the model's cross-lingual transfer capabilities.

Task Complexity: The complexity of the instruction-following tasks can impact the model's performance in zero-shot cross-lingual transfer. More complex tasks may require additional training or adaptation strategies to achieve accurate and coherent responses in different languages.

How do the findings from this study on zero-shot cross-lingual transfer apply to other generative tasks beyond instruction following?

The findings from the study on zero-shot cross-lingual transfer in instruction tuning can be extrapolated to other generative tasks in the following ways:

Transferability of Knowledge: The success of zero-shot cross-lingual transfer in instruction tuning indicates that language models can effectively transfer knowledge across languages without explicit training in each language. This transferability can be leveraged in other generative tasks, such as text generation, summarization, and dialogue systems.

Importance of Multilingual Training: The study highlights the importance of considering multilingual training data and hyperparameter tuning for successful cross-lingual transfer. This insight can be applied to other generative tasks to improve the model's performance in generating content in multiple languages.

Evaluation and Feedback: The emphasis on thorough evaluation methodologies and continuous feedback loops in the study can be extended to other generative tasks to ensure the accuracy, fluency, and relevance of the model's outputs across different languages.

Model Configuration Choices: The impact of model architecture, data size, and adaptation strategies on cross-lingual transfer demonstrated in the study can guide the selection of optimal model configurations for various generative tasks. By considering these factors, researchers can enhance the model's cross-lingual capabilities in diverse generative applications.