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

Paper recorded by Signals 4 on 2026-08-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

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 ab

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#188 most recent of 215 cs.LG papers we have recorded · ↑ newer: Unsupervised Multi-Scale Gromov-Wasserstein Hypergraph Alignment · ↓ older: $\mathcal{N}_0$-Foundation: Towards the Age of Tactile Intelligence
Cite this page: Cross-lingual Functional Vectors for Emotion Detection in Large Language Models: the #188 most recent of 215 cs.LG papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/cross-lingual-functional-vectors-for-emotion-detection-in-large-language-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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