Fine-tuning large language models (LLMs) to meet evolving safety policies is costly and impractical.Mechanistic interpretability enables inference-time control through latent activation steering, but its potential for precise, customizable safety adjustments remains underexplored.We propose SAFESTEER, a simple and effective method to guide LLM outputs by (i) leveraging category-specific steering vectors for fine-grained control, (ii) applying a gradient-free, unsupervised approach that enhances safety while preserving text quality and topic relevance without forcing explicit refusals, and (iii) eliminating the need for contrastive safe data.Across multiple LLMs, datasets, and risk categories, SAFESTEER provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content, aligning with recent findings that simple activation-steering techniques often outperform more complex alternatives.Content Warning: This paper contains examples of critically harmful language.

Shaona Ghosh, Prasoon Varshney, Makesh Narsimhan Sreedhar, Aishwarya Padmakumar, Traian Rebedea, Jibin Rajan Varghese, Christopher Parisien. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling.While in many situations, large amounts of conversational data are available for the task at hand, people lack an effective solution able to extract dialogue policies from this data.In this paper, we address this gap by first illustrating how Large Language Models (LLMs) can be instrumental in extracting dialogue policies from datasets, through the conversion of conversations into a unified intermediate representation consisting of canonical forms.We then propose a novel method for generating dialogue policies utilizing a controllable and interpretable graphbased methodology.By combining canonical forms across conversations into a flow network, we find that running graph traversal algorithms helps in extracting dialogue flows.These flows are a better representation of the underlying interactions than flows extracted by prompting LLMs.Our technique focuses on giving conversation designers greater control, offering a productivity tool to improve the process of developing dialogue policies.1 bot "Hello, how can I help you today?" intent: express greeting and offer to help user "I received this pair of boots but they are scuffed" intent: inform product received is scuffed user "I want to return and exchange them for a new pair" intent: request to return and exchange product bot "No worries, can I have your full name?"intent: ask for full name user "Alessandro Phoenix" intent: provide full name bot "And your username, email, and order ID?" intent: ask for username, email, and order id ... bot "Well then have a nice day!" intent: say goodbye bot "Hello, how can I help you today?" intent: express greeting and offer to help user "I received this pair of

In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and they are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can be determined empirically.