Cross-lingual Functional Vectors for Emotion Detection in Large Language Models

2026-08-30

AI Summary

研究通过跨语言功能向量提高大型语言模型在多语言情感识别中的表现，证明了该方法能捕捉任务相关信号并有效适应多种语言。

Abstract

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.

Problem

• Cross-lingual
• Function Vectors
• Emotion Recognition
• Large Language Models
• Multilingual

Innovation

• Cross-lingual Functional Vectors
• Emotion Detection
• Large Language Models
• Multilingual Task Adaptation
• Attention Heads