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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
Lingua Franca or Probing Artifact? Rethinking Latent Language in Multilingual LLMs
TL;DR
Multilingual language models (LLMs) exhibit varied latent language identification, revealing distinct aspects of multilingual computation rather than a unified internal language. Probes based on hidden state geometry and output-space decodability yield conflicting insights into how models process multiple languages.
✦ Why It Matters
Consider revising your probing methods to better understand multilingual model behavior and improve model training strategies.
Key Takeaways
- Explore different probing methods for multilingual models.
- Analyze hidden state geometry for insights on language mixing.
- Evaluate output-space decodability to assess language bias.
- Adopt a cautious approach to interpreting latent language findings.
How It Works
The study compares two probing methods: GMM-based representation probes analyze the geometry of hidden states to identify language mixing, while decoding-based probes assess how well outputs can be decoded in specific languages. This dual approach reveals that different methods can yield conflicting insights into how multilingual models process information.