Papers by Qifeng Su
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment (2025.acl-long)
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| Challenge: | Existing alignment benchmarks focus on sentence embeddings, but prior research has shown that neural models tend to induce a non-smooth representation space, which impact of semantic alignment evaluation on low-resource languages. |
| Approach: | They propose a novel cross-lingual alignment evaluation method based on the consistency of parallel sentences to assess model alignment. |
| Outcome: | The proposed method achieves a correlation of 0.9556 with downstream tasks performance and 0.8524 with transferability even with a small dataset. |