[공지사항]을 빙자한 안부와 근황 
Show more

Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models

Created by
  • Haebom

Author

Anirudh Sundar, Sinead Williamson, Katherine Metcalf, Barry-John Theobald, Skyler Seto, Masha Fedzechkina

Outline

This paper highlights the importance of cross-language representation alignment in multilingual large-scale language models (mLLMs), and presents a data-efficient alternative to computationally expensive fine-tuning: model interventions. In particular, we analyze the effect of manipulating the activation of mLLMs to improve cross-language representation alignment using an intervention method called “finding experts.” Specifically, we identify target neurons for manipulation for specific languages, and analyze the embedding spaces of mLLMs before and after the manipulation to show that cross-language alignment is improved. Furthermore, we experimentally demonstrate that altering the embedding space leads to improved performance on retrieval tasks, achieving up to a 2x improvement in top-1 accuracy in cross-language retrieval.

Takeaways, Limitations

•
Takeaways:
◦
We present a data-efficient method to improve cross-language representation alignment of large-scale multilingual language models without model fine-tuning.
◦
We show that cross-language retrieval performance can be improved by manipulating the embedding space using model intervention techniques such as ‘finding experts’.
◦
We achieve significant performance improvements (up to 2x improvement in top-1 accuracy) on cross-language retrieval tasks.
•
Limitations:
◦
The presented method is limited to a specific intervention technique (‘finding an expert’) and search task, and further research is needed to determine its generalizability to other tasks or intervention techniques.
◦
The analysis target should be limited to a specific mLLM and generalizability to other mLLM models should be verified.
◦
Further research is needed on the selection criteria and optimization methods of the 'Find an Expert' technique.
👍
Made with Slashpage