Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges

[2412.12686] Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2412.12686 (cs) [Submitted on 17 Dec 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges Authors: Yangfan Ye , Xiaocheng Feng , Xiachong Feng , Libo Qin , Yichong Huang , Lei Huang , Weitao Ma , Qichen Hong , Zhirui Zhang , Yunfei Lu , Xiaohui Yan , Duyu Tang , Dandan Tu , Bing Qin View a PDF of the paper titled Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges, by Yangfan Ye and 13 other authors View PDF HTML (experimental) Abstract: Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data. In this paper, we introduce and investigate cross-lingual latent transplantation (XTransplant), a probing framework which aims to further exploit the model's internalized multilingual knowledge during inference and examine its effects on the multilingual capability and cultural adaptability of LLMs. XTransplant framework enables models to harness the complementary strengths of both English and non-English resources by transplanting latent activations across languages. Through extensive analysis, we empirically demonstrate that XTransplant, a form of cross-lingual interaction, has mutually beneficial effects on the multilingual capability and cultural adaptability of LLMs, particularly for low-resource languages and cultures. We further reveal that attention modules play a pivotal role in supporting multilingual understanding, while feed-forward modules are more adept at capturing culture-specific knowledge. In addition, we conduct in-depth analysis of XTransplant's stability, effectiveness, and generalizability. By probing the upper bound performance of XTransplant, we expose the considerable underutilization of current LLMs' multilingual potential-a challenge that remains open. We hope our analysis offers a new lens for advancing cross-lingual interactions and better leveraging models' internalized multilingual knowledge. Comments: IEEE Transactions on Audio, Speech and Language Processing Subjects: Computation and Language (cs.CL) Cite as: arXiv:2412.12686 [cs.CL] (or arXiv:2412.12686v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2412.12686 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yangfan Ye [ view email ] [v1] Tue, 17 Dec 2024 09:05:30 UTC (44,710 KB) [v2] Tue, 20 May 2025 07:18:16 UTC (30,828 KB) [v3] Fri, 10 Apr 2026 04:31:27 UTC (20,817 KB) Full-text links: Access Paper: View a PDF of the paper titled Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges, by Yangfan Ye and 13 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-12 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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