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

The evolution of large language models has enabled fluent dialogue, increasing interest in the coexistence of humans and avatars. An essential aspect of achieving this coexistence involves developing sophisticated dialogue systems that can influence user behavior. In this background, we propose an effective multimodal dialogue system designed to promote consensus building with humans. Our system employs a slot-filling strategy to guide discussions and attempts to influence users with suggestions through emotional expression and intent conveyance via its avatar. These innovations have resulted in our system achieving the highest performance in a competition evaluating consensus building between humans and dialogue systems. We hope that our research will promote further discussion on the development of dialogue systems that enhance consensus building in human collaboration.