---
title: "Activation Steering"
description: "1 posts about Activation Steering, summarised, each linking to the original."
---

# Activation Steering
> 1 posts about Activation Steering, summarised, each linking to the original.

## Articles

### [On the Effectiveness-Fluency Trade-Off in LLM Conditioning: A Systematic Study](https://yomu.fyi/post/on-the-effectiveness-fluency-trade-off-in-llm-conditioning-a-systemati.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 30, 2026
- Type: Benchmark

Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet current conditioning methods are often evaluated narrowly on effectiveness without considering generation quality. This study systematically investigates a range of conditioning methods in both concept injection and removal scenarios, including efficient steering, activation steering, prompting, and supervised fine-tuning. The authors find that efficient steering methods frequently achieve their conditioning goal at a steep cost to fluency, degrading generation quality. They identify a critical previously overlooked interaction: activation steering methods are far less effective on instruction-tuned models than on their base counterparts. Simple prompting and supervised fine-tuning are viable options for concept injection but are not as good at concept removal, while cheaply computed textual metrics highly correlate with costly LLM-as-judge scores, providing practical insights for evaluation.