Large language models (LLMs) often fail to generate text in the intended target language, particularly in non-English interactions. Concurrently, recent work has explored Language Neuron Intervention (LNI) as a promising technique for steering output language. In this paper, we re-evaluate LNI in more practical scenarios—specifically with instruction-tuned models and prompts that explicitly specify the target language. Our experiments show that while LNI also shows potential in such practical scenarios, its average effect is limited and unstable across models and tasks, with a 0.83% reduction in undesired language output and a 0.1% improvement in performance. Our further analysis identifies two key factors for LNI’s limitation: (1) LNI affects both the output language and the content semantics, making it hard to control one without affecting the other, which explains the weak performance gains. (2) LNI increases the target language token probabilities, but they often remain below the top-1generation threshold, resulting in failure to generate the target language in most cases. Our results highlight both the potential and limitations of LNI, paving the way for future improvements.

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.