Soft Token Alignment for Cross-Lingual Reasoning
Quick Answer
The proposed SOLAR method enhances multilingual large language models by aligning soft-token representations across languages, improving accuracy by up to 17.7 points on reasoning benchmarks.
Quick Take
The proposed SOLAR method enhances multilingual large language models by aligning soft-token representations across languages, improving accuracy by up to 17.7 points on reasoning benchmarks. This approach reduces language-specific divergences, particularly benefiting low-resource languages and preserving shared semantic structures during reasoning.
Key Points
- SOLAR aligns soft-token representations using English as a pivot for multilingual models.
- Achieves up to +17.7 accuracy improvement on four multilingual reasoning benchmarks.
- Largest gains observed in low-resource languages, enhancing their performance.
- Reduces language-cluster separability, preserving semantic structure across languages.
- Strengthens final-layer cross-lingual similarity in multilingual reasoning tasks.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 26466v1 Announce Type: new Abstract: Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior work suggests that intermediate representations can be relatively language-agnostic, but generation becomes increasingly language-specific as models commit to discrete output tokens.
This is problematic because language-specific lexical choices can cause semantically equivalent reasoning paths to diverge across languages. These divergences motivate searching for a cross-lingual alignment signal that is less tied to any single vocabulary item or script. We propose SOLAR, an auxiliary objective for supervised fine-tuning that aligns soft-token representations across languages, using English as a pivot.
Soft tokens are probability-weighted mixtures over the vocabulary embeddings, yielding continuous representations that can aggregate information from semantically related tokens across languages. We then align each non-English soft-token summary to its English counterpart in the shared embedding space. Across four multilingual reasoning benchmarks, SOLAR improves accuracy by up to +17. 7 points over the base model and +3. 8 over standard supervised fine-tuning, with the largest gains on low-resource languages.
SOLAR also strengthens final-layer cross-lingual similarity and substantially reduces language-cluster separability, suggesting that aligning soft-token representations helps preserve shared semantic structure during multilingual reasoning.
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