Function Vectors

Follow this topic Ask about this topic

Latest papers

8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Cross-lingual Functional Vectors for Emotion Detection in Large Language Models
Aug 30, 2026Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen +1Function Vectors Cross-Lingual Transfer
Japan Advanced Institute of Science and Technology
Revisiting the Systematicity in Negation in the Era of In-Context Learning
Jun 15, 2026Hitomi Yanaka, Taisei YamamotoTask Vectors In-Context Learning
The University of Tokyo · Riken · Tohoku University
Fast & Faithful Function Vectors
Jun 3, 2026Minh An Pham, Anton Segeler, Thomas Wiegand +4Task Vectors LLM Interpretability
Fraunhofer Heinrich-Hertz-Institute, Berlin, Germany · Technische Universität Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany +1
Function-Vector Heads Are Two Populations: Writers and Cancellers in In-Context Learning
May 25, 2026Han-yu WangTransformer Interpretability Task Vectors
The University of Hong Kong
How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning
May 15, 2026Entang Wang, Yiwei Wang, Aleksandra Bakalova +1Transformer Interpretability Task Vectors
Saarland Informatics Campus, Saarland Univesity, Saarbrücken, Germany.
Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation
Apr 21, 2026Nurkhan Laiyk, Gerard I. Gállego, Javier Ferrando +1Multilingual Language Models Task Vectors
Mohamed bin Zayed University of Artificial Intelligence · Universitat Politècnica de Catalunya · Cantina Labs
Steerable but Not Decodable: Function Vectors Operate Beyond the Logit Lens
Apr 3, 2026Mohammed Suhail B NadafFunction Vectors Transformer Interpretability
Independent
Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
Feb 4, 2026Jonas Rohweder, Subhabrata Dutta, Iryna GurevychTransformer Interpretability Latent Variable Models
UKP Lab, TU Darmstadt

Aug 30, 2026J/K move · Enter open · S save

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

Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, Shogo Okada

Japan Advanced Institute of Science and Technology

Function Vectors Cross-Lingual Transfer Multilingual Language Models Emotion Recognition

Open paper AskPDF arXiv

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.