arXiv:2604.19678v2 Announce Type: replace Abstract: Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that multilingual model representations can be language-agnostic, it remains unclear whether the same holds for function vectors. We study whether FVs exhibit language-agnosticity, using machine translation as a case study. Across three decoder-only multilingual LLMs, we find that translation FVs extracted from a single English$\to$X direction transfer to other target languages, consistently improv
This research addresses a fundamental question in AI development regarding the generalizability and transferability of function vectors in multilingual contexts, building on recent advances in in-context learning and multilingual LLMs.
Understanding the language-agnosticity of function vectors has significant implications for developing more efficient, robust, and generalizable AI models, particularly for global applications like machine translation.
The finding that translation function vectors can transfer across target languages simplifies the development of multilingual AI, potentially reducing the need for language-specific training data or architectures for certain tasks.
- · Multilingual LLM developers
- · Machine translation service providers
- · AI researchers in natural language processing
- · Businesses operating globally
- · Developers focused solely on single-language AI models
- · Companies relying on language-specific deep learning architectures for translati
Increased efficiency in developing and deploying AI models for diverse linguistic environments.
Acceleration in the creation of truly universal AI agents capable of operating across many languages with minimal retraining.
Ethical and societal implications arising from highly generalized AI capabilities, affecting information access and cultural preservation.
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