CLP-Transfer: Cross-Lingual and Progressive Transfer Learning

2024-07-15 · Source: Research feeds | TransferLab — appliedAI Institute · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, medium

Summary

The CLP-Transfer method offers a new approach for cross-lingual language model transfer, simplifying the process by leveraging token overlap and a small pre-trained model with the target tokenizer, thus eliminating the need for fastText embeddings or bilingual dictionaries. It operates on two key assumptions: significant vocabulary overlap between source and target languages, and comparable token embeddings across models of different sizes using the same tokenizer. Experiments with GPT-2 and BLOOM models, using German OSCAR and GC4 datasets, showed that CLP-Transfer achieved better perplexity scores than from-scratch training with fewer tokens. However, its zero-shot performance on German downstream tasks like sentiment analysis and hate speech classification was generally disappointing, often not outperforming a random baseline, which the authors attribute to dataset splits, perplexity's limitations as a proxy, lack of fine-tuning, and dataset quality issues.

Key takeaway

For AI Scientists developing cross-lingual language models, CLP-Transfer offers a simpler initialization method that significantly reduces training tokens needed to achieve good perplexity. However, you should anticipate that models initialized with CLP-Transfer may require extensive fine-tuning and prompt engineering to perform effectively on specific downstream tasks, as zero-shot results were often comparable to a random baseline.

Key insights

CLP-Transfer simplifies cross-lingual model transfer using token overlap and a small helper model, but shows limited downstream task performance.

Principles

• Significant token overlap enables cross-lingual transfer.
• Token embeddings are comparable across model sizes with same tokenizer.

Method

CLP-Transfer copies overlapping token embeddings and computes non-overlapping ones using cosine similarity with a small helper model, then transfers remaining parameters.

In practice

• Use CLP-Transfer for efficient perplexity reduction.
• Consider fine-tuning for better downstream task performance.

Topics

• Cross-lingual Transfer
• CLP-Transfer Method
• Token Embeddings
• Language Models
• Zero-shot Evaluation

Code references

• malteos/clp-transfer
• cpjku/wechsel

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Research feeds | TransferLab — appliedAI Institute.