Energy-Driven Steering: Reducing False Refusals in Large Language Models

• URL: http://arxiv.org/abs/2510.08646v1
• Date: Thu, 09 Oct 2025 06:01:41 GMT
• Title: Energy-Driven Steering: Reducing False Refusals in Large Language Models
• Authors: Eric Hanchen Jiang, Weixuan Ou, Run Liu, Shengyuan Pang, Guancheng Wan, Ranjie Duan, Wei Dong, Kai-Wei Chang, XiaoFeng Wang, Ying Nian Wu, Xinfeng Li,
• Abstract summary: We introduce Energy-Driven Steering (EDS), a novel, fine-tuning free framework designed to resolve this challenge through dynamic, inference-time intervention.<n>We trained a lightweight, external Energy-Based Model (EBM) to assign high energy to undesirable (false refusal or jailbreak) states and low energy to desirable (helpful response or safe reject) ones.<n>We use the gradient of the energy function to dynamically steer the LLM's hidden states to low energy regions, correcting the model to generate a desirable response in real-time without modifying its weights.
• Score: 80.09252175869858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety alignment of large language models (LLMs) faces a key challenge: current alignment techniques often only focus on improving safety against harmful prompts, causing LLMs to become over-cautious and refuse to respond to benign prompts. Therefore, a key objective of safe alignment is to enhance safety while simultaneously reducing false refusals. In this paper, we introduce Energy-Driven Steering (EDS), a novel, fine-tuning free framework designed to resolve this challenge through dynamic, inference-time intervention. We trained a lightweight, external Energy-Based Model (EBM) to assign high energy to undesirable (false refusal or jailbreak) states and low energy to desirable (helpful response or safe reject) ones. During inference, EBM maps the LLM's internal activations to an "energy landscape". We use the gradient of the energy function to dynamically steer the LLM's hidden states to low energy regions, correcting the model to generate a desirable response in real-time without modifying its weights. This method decouples behavioral control from the model's core knowledge, offering a flexible solution with minimal computational overhead. Extensive experiments across a wide range of models show our method successfully achieves this objective: it substantially lowers false refusal rates. For example, raising compliance on the ORB-H benchmark from 57.3% to 82.6% while maintaining the baseline safety performance. Our work presents an effective paradigm for building LLMs that achieve both low false refusal rates and high safety.

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