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Training-Free Task Vectors for LLM Behavioral Control

Paper recorded by Signals 4 on 2026-09-08 in cs.AI. Abstract reproduced from arXiv; link to the original below.

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

Category: cs.AI · 人工智能 · first seen 2026-09-09

Abstract

Task vectors enable post-training model editing by identifying semantically meaningful directions in weight space, typically computed as the difference between a fine-tuned model and its pretrained initialization. However, this reliance on fine-tuning makes discovering such directions costly and limits the practicality of post-training model editing. To address this limitation, we introduce Traini

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#159 most recent of 300 cs.AI papers we have recorded · ↑ newer: Time-Varying Data as Sheaves: an Invitation to Narratives · ↓ older: The Audit Decides the Verdict: Instrument Effects Rival Demographic Bi
Cite this page: Training-Free Task Vectors for LLM Behavioral Control: the #159 most recent of 300 cs.AI papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/training-free-task-vectors-for-llm-behavioral-control.html
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