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

• URL: http://arxiv.org/abs/2608.29613v1
• Date: Sun, 30 Aug 2026 07:08:36 GMT
• Title: Cross-lingual Functional Vectors for Emotion Detection in Large Language Models
• Abstract summary: Function vectors (FVs) have emerged as a promising mechanism for steering the behavior of large language models.<n>We investigate the cross-lingual transferability of FVs using multilingual multi-label emotion recognition.
• Score: 2.3567984677073945
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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