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    <abstract>Controllable generation requires language models to realize output characteristics such as reading level, politeness, and toxicity. Existing steering methods are often indirect, require access to internal activations, or depend on auxiliary trained models. We propose SWAI, a training-free inference-time method that addresses these limitations by steering directly in logit space using corpus-derived token statistics. SWAI computes z-normalized one-vs-rest log-odds scores from labeled corpora and biases high-scoring tokens only within the model’s top-K candidate set, allowing control to favor target-characteristic tokens while preserving contextually plausible choices. Across readability, politeness, and toxicity control, SWAI consistently improves over prompt-based and prior logit-level baselines without modifying model parameters, accessing internal layers, or training an auxiliary model. Selectivity and lookup-table ablations show that the gains come from target-specific statistical scores rather than generic logit perturbation. These results indicate that effective steering does not require learned controllers when the logit intervention is guided by target-specific statistics under high-probability candidates.</abstract>
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%0 Conference Proceedings
%T Steering Language Models Before They Speak: Logit-Level Interventions
%A An, Hyeseon
%A Park, Shinwoo
%A Jin, Hyundong
%A Han, Yo-Sub
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F an-etal-2026-steering
%X Controllable generation requires language models to realize output characteristics such as reading level, politeness, and toxicity. Existing steering methods are often indirect, require access to internal activations, or depend on auxiliary trained models. We propose SWAI, a training-free inference-time method that addresses these limitations by steering directly in logit space using corpus-derived token statistics. SWAI computes z-normalized one-vs-rest log-odds scores from labeled corpora and biases high-scoring tokens only within the model’s top-K candidate set, allowing control to favor target-characteristic tokens while preserving contextually plausible choices. Across readability, politeness, and toxicity control, SWAI consistently improves over prompt-based and prior logit-level baselines without modifying model parameters, accessing internal layers, or training an auxiliary model. Selectivity and lookup-table ablations show that the gains come from target-specific statistical scores rather than generic logit perturbation. These results indicate that effective steering does not require learned controllers when the logit intervention is guided by target-specific statistics under high-probability candidates.
%U https://aclanthology.org/2026.emnlp-main.2159/
%P 49123-49143

Markdown (Informal)

[Steering Language Models Before They Speak: Logit-Level Interventions](https://aclanthology.org/2026.emnlp-main.2159/) (An et al., EMNLP 2026)

• Steering Language Models Before They Speak: Logit-Level Interventions (An et al., EMNLP 2026)

ACL

• Hyeseon An, Shinwoo Park, Hyundong Jin, and Yo-Sub Han. 2026. Steering Language Models Before They Speak: Logit-Level Interventions. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49123–49143, Budapest, Hungary. Association for Computational Linguistics.