![[2510.12121] Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.12121] Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Summary
This paper introduces a method for precise control of attribute intensities in Large Language Models (LLMs) through targeted representation editing, enhancing adaptability to user specifications.
Why It Matters
As LLMs become integral in various applications, the ability to control output attributes with precision is crucial for meeting diverse user expectations. This research addresses current limitations in LLM alignment methods, offering a more reliable approach to generating tailored outputs.
Key Takeaways
- Introduces a target-reaching approach for attribute intensity control in LLMs.
- Utilizes a lightweight value function for predicting attribute intensities.
- Implements gradient-based interventions for precise navigation of model outputs.
- Demonstrates high accuracy in steering text generation to user-defined attributes.
- Shows efficiency improvements across various downstream tasks.
Computer Science > Artificial Intelligence arXiv:2510.12121 (cs) [Submitted on 14 Oct 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing Authors:Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen, Tong Yu, Lingkai Kong, Sudheer Chava, Chao Zhang View a PDF of the paper titled Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing, by Rongzhi Zhang and 7 other authors View PDF HTML (experimental) Abstract:Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse user expectations. Current LLM alignment methods, however, typically provide only directional or open-ended guidance, failing to reliably achieve exact attribute intensities. We address this limitation with three key designs: (1) reformulating precise attribute intensity control as a target-reaching problem, rather than simple maximization; (2) training a lightweight value function via temporal-difference learning to predict final attribute intensity scores from partial generations, thereby steering LLM outputs; and (3) employing gradient-based interventions on hidden representations to navigate the model precisely towards specific attribute intensity targets. Our method enables fine-grained, continuous control over attribute intensities, moving beyo...
