论文发现功能向量能跨语言传递任务信号，无需演示即可提升多语言情感识别性能，比少样本学习更高效。
该研究探索了功能向量(FVs)在多语言多标签情感识别任务中的跨语言迁移能力。研究显示，从源语言提取的功能向量能在零样本设置中显著提升目标语言性能，无需推理时提供演示。每个大语言模型在构建有效功能向量时表现出相对稳定的最佳注意力头范围，且这一模式在不同语言中保持一致。
Cross-lingual Functional Vectors for Emotion Detection in Large Language Models
Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specific latent direction representations derived from in-context demonstrations. While prior studies have shown that FVs can recover task behavior in structured in-context learning settings, their effectiveness on semantically complex tasks and their ability to generalize across languages remain underexplored. We investigate the cross-lingual transferability of FVs using multilingual multi-label emotion recognition as a challenging semantic classification benchmark. Specifically, we examine whether FVs extracted from a source language can steer task behavior in another language under both standard clean and perturbed zero-shot settings without providing demonstrations during inference. Across diverse cross-lingual settings, applying FVs substantially improves performance, suggesting that FVs capture language-agnostic, task-relevant signals rather than purely language-specific lexical patterns, and highlighting their potential as a lightweight and transferable mechanism for multilingual task adaptation. We observe that each LLM exhibits a relatively stable optimal range of attention heads for constructing effective FVs, and the pattern remains consistent across languages. In addition, FVs can partially replicate the task-steering effects of standard few-shot in-context learning while avoiding the computational overhead of processing multiple demonstrations, making them effective for large-scale practical applications. Our code is available at https://github.com/yingjie7/cross_lingual_fvs.