用线性变换高效对齐文本嵌入空间，无需平行语料。
mini-vec2vec: Scaling Universal Geometry Alignment with Linear Transformations
- 通过伪平行向量匹配、线性变换拟合与迭代优化三阶段实现对齐。
- 效率比原方法高数个数量级，结果相当或更优。
- 线性映射可解释性强，适合跨领域推广。
我们基于vec2vec——一种无需平行数据即可对齐文本嵌入空间的方法——提出mini-vec2vec，一个更简单、高效的替代方案。vec2vec虽能实现近似完美对齐，但计算成本高且不稳定。mini-vec2vec仅需更低的计算开销，且具有高度鲁棒性，其学习到的映射为线性变换。方法包含三个主要阶段：伪平行嵌入向量的初步匹配、变换拟合以及迭代精化。该线性替代方案在效率上较原vec2vec提升数个数量级，同时保持或超越其性能。算法步骤可解释性强，显著提升可扩展性，为新领域应用开辟了新可能。
原文摘要 · Abstract (English)
We build upon vec2vec, a procedure designed to align text embedding spaces without parallel data. vec2vec finds a near-perfect alignment, but it is expensive and unstable. We present mini-vec2vec, a simple and efficient alternative that requires substantially lower computational cost and is highly robust. Moreover, the learned mapping is a linear transformation. Our method consists of three main stages: a tentative matching of pseudo-parallel embedding vectors, transformation fitting, and iterative refinement. Our linear alternative exceeds the original instantiation of vec2vec by orders of magnitude in efficiency, while matching or exceeding their results. The method's stability and interpretable algorithmic steps facilitate scaling and unlock new opportunities for adoption in new domains and fields.
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