Summary of Probing the Emergence Of Cross-lingual Alignment During Llm Training, by Hetong Wang et al.
Probing the Emergence of Cross-lingual Alignment during LLM Training
by Hetong Wang, Pasquale Minervini, Edoardo M. Ponti
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the remarkable zero-shot cross-lingual transfer performance achieved by Multilingual Large Language Models (LLMs). Researchers suggest that LLMs’ ability to align languages without explicit supervision from parallel sentences is crucial for this performance. While it’s known that representations of translationally equivalent sentences in different languages are similar after convergence, the study aimed to understand how cross-lingual alignment emerges during pre-training. Using intrinsic probing techniques, the authors analyzed BLOOM, a multilingual autoregressive LLM, across various training steps and model scales. The findings show a high correlation between neuron overlap and downstream performance, supporting the hypothesis on conditions leading to effective cross-lingual transfer. Interestingly, the study also detected degradation of implicit alignment and multilingual abilities in certain phases of pre-training, providing new insights into the dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models can work with different languages without needing specific training data for each language. Researchers wanted to know why these models can pick up on similar patterns between languages and transfer their knowledge easily. They used special techniques to see which parts of the model were responsible for this ability. By analyzing a popular language model called BLOOM, they found that when certain parts of the model are working well, it leads to good performance with different languages. The study also showed that sometimes these models can even lose their ability to work across languages during training. |
Keywords
» Artificial intelligence » Alignment » Autoregressive » Language model » Zero shot
