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        <title>Can Language Neuron Intervention Reduce Non-Target Language Output?</title>
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    <abstract>Large language models (LLMs) often fail togenerate text in the intended target language,particularly in non-English interactions. Con-currently, recent work has explored LanguageNeuron Intervention (LNI) as a promising tech-nique for steering output language. In thispaper, we re-evaluate LNI in more practicalscenarios—specifically with instruction-tunedmodels and prompts that explicitly specify thetarget language. Our experiments show thatwhile LNI also shows potential in such practi-cal scenarios, its average effect is limited andunstable across models and tasks, with a 0.83%reduction in undesired language output and a0.1% improvement in performance. Our furtheranalysis identifies two key factors for LNI’slimitation: (1) LNI affects both the output lan-guage and the content semantics, making ithard to control one without affecting the other,which explains the weak performance gains. (2)LNI increases the target language token proba-bilities, but they often remain below the top-1generation threshold, resulting in failure to gen-erate the target language in most cases. Ourresults highlight both the potential and limi-tations of LNI, paving the way for future im-provements</abstract>
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%0 Conference Proceedings
%T Can Language Neuron Intervention Reduce Non-Target Language Output?
%A Xie, Suchun
%A Kim, Hwichan
%A Sasaki, Shota
%A Yamada, Kosuke
%A Suzuki, Jun
%Y Belinkov, Yonatan
%Y Mueller, Aaron
%Y Kim, Najoung
%Y Mohebbi, Hosein
%Y Chen, Hanjie
%Y Arad, Dana
%Y Sarti, Gabriele
%S Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-346-3
%F xie-etal-2025-language
%X Large language models (LLMs) often fail togenerate text in the intended target language,particularly in non-English interactions. Con-currently, recent work has explored LanguageNeuron Intervention (LNI) as a promising tech-nique for steering output language. In thispaper, we re-evaluate LNI in more practicalscenarios—specifically with instruction-tunedmodels and prompts that explicitly specify thetarget language. Our experiments show thatwhile LNI also shows potential in such practi-cal scenarios, its average effect is limited andunstable across models and tasks, with a 0.83%reduction in undesired language output and a0.1% improvement in performance. Our furtheranalysis identifies two key factors for LNI’slimitation: (1) LNI affects both the output lan-guage and the content semantics, making ithard to control one without affecting the other,which explains the weak performance gains. (2)LNI increases the target language token proba-bilities, but they often remain below the top-1generation threshold, resulting in failure to gen-erate the target language in most cases. Ourresults highlight both the potential and limi-tations of LNI, paving the way for future im-provements
%U https://aclanthology.org/2025.blackboxnlp-1.26/
%P 452-466

Markdown (Informal)

[Can Language Neuron Intervention Reduce Non-Target Language Output?](https://aclanthology.org/2025.blackboxnlp-1.26/) (Xie et al., BlackboxNLP 2025)

• Can Language Neuron Intervention Reduce Non-Target Language Output? (Xie et al., BlackboxNLP 2025)

ACL

• Suchun Xie, Hwichan Kim, Shota Sasaki, Kosuke Yamada, and Jun Suzuki. 2025. Can Language Neuron Intervention Reduce Non-Target Language Output?. In Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 452–466, Suzhou, China. Association for Computational Linguistics.