aiqbench analysis
This research introduces an optimal control framework to enforce target-language adherence in multilingual models, achieving performance comparable to existing activation steering techniques with reduced hyperparameter tuning. The study directly addresses a core challenge in machine learning by improving the efficiency and reliability of cross-lingual text generation without relying on incidental AI features.
Target-Language Generation in Multilingual Models: Activation Steering and Optimal Control
arXiv:2609.16967v1 Announce Type: new Abstract: Ensuring that multilingual language models generate coherent text in a specific target language is a major issue in multilingual language modeling. We develop an optimal control method for target-language text generation as well as a framework for evaluating the quality of generated text in terms of language adherence, linguistic coherence, and semantic coherence. We find that the proposed method performs at least as well as the prominent difference-in-means activation steering method for the majority of models tested, with substantially less hyperparameter tuning required.
Abstract:Ensuring that multilingual language models generate coherent text in a specific target language is a major issue in multilingual language modeling. We develop an optimal control method for target-language text generation as well as a framework for evaluating the quality of generated text in terms of language adherence, linguistic coherence, and semantic coherence. We find that the proposed method performs at least as well as the prominent difference-in-means activation steering method for the majority of models tested, with substantially less hyperparameter tuning required.
Computation and Language (cs.CL)
arXiv:2609.16967 [cs.CL]
(or arXiv:2609.16967v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.16967 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 178 words
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