E n ST o M : Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance (2025)

Abstract: Small large language models (sLLMs) offer the advantage of being lightweight and efficient, which makes them suitable for resource-constrained environments. However, sLLMs often struggle to maintain topic consistency in task-oriented dialogue systems, which is critical for scenarios such as service chatbots. Specifically, it is important to ensure that the model denies off-topic or malicious inputs and adheres to its intended functionality so as to prevent potential misuse and uphold reliability. Towards this, existing activation engineering approaches have been proposed to manipulate internal activations during inference. While these methods are effective in certain scenarios, our preliminary experiments reveal their limitations in ensuring topic adherence. Therefore, to address this, we propose a novel approach termed En tropy-scaled S teering vectors for To pic M aintenance (EnSToM). EnSToM dynamically adjusts the steering intensity based on input uncertainty, which allows the model to handle off-topic distractors effectively while preserving on-topic accuracy. Our experiments demonstrate that EnSToM achieves significant performance gain with a relatively small data size compared to fine-tuning approaches. By improving topic adherence without compromising efficiency, our approach provides a robust solution for enhancing sLLM-based dialogue systems.

## introduction recent advances in large language models (llms) have enabled the development of sophisticated conversational systems across a wide range of services @xcite . these systems are increasingly being adopted by organizations for applications such as customer support, conversational assitants, and internal process guidance. however, 1 the source code is available at https://github.com/ linkyouhj/enstom scenario: ordering a new batch of personal checks. openly available api-based large-scale models often face limitations in terms of compliance with strict data privacy policies and security regulations. furthermore, large-scale open-source models demand significant computational resources, which results in high operational costs for deployment. in this context, sllms have emerged as a practical alternative @xcite by offering lightweight and resource-efficient solutions for production environments. since these models enable organizations to achieve robust conversational capabilities without the extensive computational costs associated with larger models, they are a compelling choice for a variety of applications. despite their impressive performance on general tasks, llms face challenges when deployed in real-world scenarios that demand consistent maintenance of specific constraints like business contexts, or scenario-driven dialogues @xcite . this issue becomes especially pronounced with sllms @xcite , as their limited capacity makes it even harder to ensure scenario consistency over extended user interactions (figure 1 ). the inability to maintain a prescribed scenario directly undermines a service chatbot's intended functionality; if it cannot adhere to a given workflow, policy, or domain rule, it fails to deliver the expected user experience, which potentially leads to misinformation, reduced trustworthiness, and even safety concerns such as inadvertently disclosing sensitive information @xcite . consequently, the capability of an llm to reliably uphold scenario constraints and follow specified directives is not merely an enhancement but a necessity in real-world applications. numerous alignment techniques have been proposed to address this issue, with two prominent approaches being fine-tuning and prompt engineering. fine-tuning the model with domain-specific, high-quality data can effectively realign its internal parameters to suit particular constraints. however, this process demands substantial resources in terms of data collection, annotation, and computational cost, which makes it impractical in covering every possible scenario. meanwhile, prompt engineering techniques offer a more lightweight and less resource-intensive solution. while prompt-based methods have demonstrated efficacy in steering model behavior, their effectiveness often diminishes in complex, nuanced scenarios @xcite where detailed instructions and long-term context-maintenance are required. in light of these limitations, there is a clear need for new, more flexible methods that can help llms to consistently maintain scenario adherence without incurring the substantial overhead of extensive fine-tuning or relying solely on prompt design. to this end, we propose a novel and lightweight approach termed entropy-scaled steering vectors for topic maintenance (enstom) based on activation addition, which steers a model's generation at inference time without altering its parameters. by injecting a carefully derived steering vector into the model's intermediate activations, we can gently nudge the llm towards maintaining scenario consistency. however, our preliminary experiment showed that straightforward application of activation addition cause undesired steering even for ontopic inputs, potentially degrading the user experience or interfering with correct responses. to address this, we introduce entropy-based coefficient scaling that leverages intrinsic model signals-specifically, layer-wise generation entropy-to differentiate between on-topic and distractor inputs. this is motivated by our key observation that the entropy distribution varies depending on whether the input is on-topic or a distractor. by dynamically adjusting the steering vector's strength based on this entropy information, our method is able to enforce scenario adherence more diligently for distractor inputs while preserving the model's natural behavior for on-topic interactions. this approach offers a resource-efficient alignment strategy that can enhance existing promptbased methods without the need for extensive retraining or exhaustive scenario-specific data collection. in this paper, we detail the design of our method, present an in-depth analysis of its performance, and demonstrate its ability to promote scenario adherence while minimizing adverse effects on normal inputs. our main contributions can therefore be summarized as follows: ## preliminaries this section provides an overview of the fundamental concepts and methodologies that form the basis of our approach towards maintaining topic consistency in task-oriented dialogues. it also includes a brief description of the source dataset and the methodology for extracting steering vectors. ## proposed methodology according to preliminary experiments ( ## conclusion in this paper, we introduced enstom, a lightweight and training-free method for enhancing topic consistency in task-oriented dialogue systems using entropy-scaled steering vectors. by integrating steering vector with an entropy-based coefficient scaling mechanism, our approach dynamically adjusts steering intensity based on the model's generation entropy. evaluations on the canttalkabout-this dataset demonstrated a significant improve-ment in distractor accuracy while preserving ontopic performance, which results in an increase of overall accuracy. furthermore, experiments across different models, domains, and tasks validated the generalizability of our method. even with limited steering vector samples, enstom remained effective, making it suitable for low-resource settings. additionally, our layer-wise entropy analysis provides valuable insights into llm behavior, contributing to improved interpretability. these findings support the development of adaptive and scenario-consistent dialogue systems for real-world applications.