Improving Cross-Lingual Transfer in Decoder Language Models Through Pretraining with Active Forgetting

Keskeiset käsitteet

Pretraining decoder-only LLMs with active forgetting, a technique involving periodic resetting of token embeddings, enhances their cross-lingual transfer capabilities and allows for better adaptation to new languages without sacrificing performance in other languages.

Tiivistelmä

• Bibliographic Information: Aggarwal, D., Sathe, A., & Sitaram, S. (2024). Exploring Pretraining via Active Forgetting for Improving Cross Lingual Transfer for Decoder Language Models. arXiv preprint arXiv:2410.16168.
• Research Objective: This research paper investigates the effectiveness of pretraining with active forgetting for improving cross-lingual transfer in decoder-only Large Language Models (LLMs).
• Methodology: The researchers pretrained decoder-only LLMs using active forgetting, a technique where token embeddings are reset after a certain number of training steps. They then adapted these models to new languages using vocabulary expansion and instruction-finetuned them on English data. The performance of these models was evaluated on various multilingual benchmarks and compared to baselines.
• Key Findings: The study found that LLMs pretrained with active forgetting demonstrated superior cross-lingual transfer capabilities compared to baseline models. They exhibited better perplexity and isotropy, indicating improved multilingual representations. Moreover, these models outperformed baselines on six out of seven multilingual benchmarks, demonstrating their effectiveness in adapting to new languages without significant performance degradation in other languages.
• Main Conclusions: Pretraining with active forgetting significantly enhances the cross-lingual transfer abilities of decoder-only LLMs. This technique leads to better multilingual representations, enabling the models to adapt to new languages more effectively while maintaining performance across different language families.
• Significance: This research contributes to the field of cross-lingual transfer learning by introducing a novel pretraining approach for decoder-only LLMs. It addresses the challenge of adapting LLMs to multiple languages, particularly in low-resource scenarios, and paves the way for developing more language-agnostic LLMs.
• Limitations and Future Research: The study primarily focused on LLMs of moderate size and a limited number of languages. Further research is needed to evaluate the effectiveness of active forgetting pretraining on larger LLMs and a wider range of languages. Additionally, exploring the impact of active forgetting on other downstream tasks beyond the ones considered in this study would be beneficial.

Tilastot

AFA models outperform the baselines on 6 out of 7 multilingual benchmarks.
|Vmerged| = 48000

Lainaukset

"We show that LLMs pretrained with active forgetting are highly effective when adapting to new and unseen languages."
"Through extensive experimentation, we find that LLMs pretrained with active forgetting are able to learn better multilingual representations which translates to better performance in many downstream tasks."
"We illustrate that base LLMs pretrained with active forgetting lead to higher quality multilingual representations."

Tärkeimmät oivallukset

Exploring Pretraining via Active Forgetting for Improving Cross Lingual Transfer for Decoder Language Models

by Divyanshu Ag... klo arxiv.org 10-22-2024

https://arxiv.org/pdf/2410.16168.pdf

Syvällisempiä Kysymyksiä

How does the performance of active forgetting pretraining compare to other cross-lingual transfer learning techniques, such as multilingual adapters or language-specific fine-tuning?

Active forgetting pretraining, as presented in the paper, demonstrates a unique approach to cross-lingual transfer compared to techniques like multilingual adapters or language-specific fine-tuning. Here's a breakdown:

Active Forgetting Pretraining: This method focuses on enhancing the base model's "language plasticity" during pretraining. By periodically resetting token embeddings, the model is forced to learn more general language representations, making it more adaptable to new languages later on. This technique shows promising results in improving cross-lingual transfer without relying heavily on language-specific data during fine-tuning.

Multilingual Adapters: These are lightweight modules added to a pre-trained model. They are trained on language-specific data to adapt the model to new languages without modifying the original model's parameters extensively. While effective, they require separate adapters for each language, potentially increasing complexity.

Language-Specific Fine-tuning: This involves fine-tuning the entire pre-trained model on data from a specific target language. This approach can lead to high performance on the target language but might result in catastrophic forgetting, where the model's performance on other languages degrades.
Comparison:

Data Efficiency: Active forgetting pretraining shines in low-resource scenarios as it improves cross-lingual transfer without extensive language-specific data during fine-tuning. Adapters and fine-tuning often require substantial language-specific data.

Computational Cost: Active forgetting might increase pretraining time but reduces the need for extensive language-specific fine-tuning later. Adapters add complexity, while fine-tuning requires retraining the entire model.

Performance: The paper suggests active forgetting pretraining leads to competitive or superior performance on multilingual benchmarks compared to baselines. However, a direct comparison with adapters and fine-tuning on the same tasks and datasets would provide a more definitive answer.

Could the periodic resetting of token embeddings during pretraining lead to a loss of previously learned information, potentially hindering performance in certain scenarios?

Yes, the periodic resetting of token embeddings during active forgetting pretraining could lead to a loss of previously learned information, potentially impacting performance in certain scenarios.
Here's why:

Disruption of Learned Representations: Resetting embeddings disrupts the model's learned representations of those tokens. While the model is forced to relearn these representations, it might not perfectly recover the previously acquired knowledge.

Impact on Frequent vs. Rare Tokens: Frequent tokens, encountered often during pretraining, are likely to be relearned effectively after each reset. However, representations for rare tokens might not be fully recovered, potentially hindering the model's performance on tasks involving those tokens.

Trade-off Between Plasticity and Stability: Active forgetting aims to increase language plasticity, making the model more adaptable. However, this comes at the cost of stability in learned representations. Finding the optimal balance between plasticity and stability is crucial.
Potential Mitigation Strategies:

Gradual Resetting: Instead of abruptly resetting all embeddings, a gradual approach could be explored, where a subset of embeddings are reset at each step.

Curriculum Learning: Introducing a curriculum during pretraining, where the model is first trained on a diverse multilingual dataset before incorporating active forgetting, might mitigate information loss.

Hybrid Approaches: Combining active forgetting with other techniques, such as embedding regularization or knowledge distillation, could help retain important information while promoting plasticity.

What are the implications of this research for developing more inclusive and accessible language technologies that cater to a diverse range of languages and cultures?

This research on active forgetting pretraining holds significant implications for developing more inclusive and accessible language technologies:

Reducing Language Barriers: By improving cross-lingual transfer, active forgetting has the potential to bridge the gap between high-resource languages like English and low-resource languages. This could lead to the development of language models that perform well across a diverse range of languages, making information and technology accessible to a wider audience.

Preserving Linguistic Diversity: The ability to train language models effectively on a multitude of languages without requiring massive amounts of data for each language can help preserve linguistic diversity. This is particularly important for languages with limited digital resources.

Facilitating Cross-Cultural Communication: Improved cross-lingual transfer can facilitate more accurate and natural machine translation, breaking down communication barriers between cultures and fostering greater understanding and collaboration.

Lowering Development Costs: Active forgetting's potential for data efficiency could lower the cost of developing multilingual language technologies. This is particularly beneficial for organizations and communities with limited resources.
However, ethical considerations are crucial:

Bias Amplification: While promoting inclusivity, it's essential to ensure that active forgetting doesn't amplify existing biases present in training data. Careful data curation and bias mitigation techniques are paramount.

Equitable Performance: Efforts should be made to ensure that the performance improvements from active forgetting are distributed equitably across languages, avoiding scenarios where certain languages benefit disproportionately.

Cultural Sensitivity: Developing language technologies for diverse cultures requires a deep understanding and respect for cultural nuances. Active forgetting should be implemented in a way that is sensitive to these nuances and avoids perpetuating harmful stereotypes.