TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering
Ryandito DiandaruAhmed ElshabrawyFadli Aulawi Al GhiffariIkhlasul Akmal HanifAlham Fikri Aji
Oct 2026
Artificial IntelligenceNatural Language Processing
Abstract
We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest a different concept. We propose TimpaTeks, an automatic in-place text modification mechanism using DLMs. Experiments on IMDB movie reviews (sentiment) and a synthetic Cats and Dogs Dataset (arbitrary, more unconventional concept steering) show that TimpaTeks provides a feasible novel mechanism to steer diffusion language model outputs in-place. TimpaTeks enables in-place modification while simultaneously lowers sentence perplexity and retaining the original sentence structre without the need of instruction tuned models. TimpaTeks is also computationally cheaper than prompt-based DLM steering, as it performs denoising in-place rather than constructing an additional prompt-conditioned output sequence.
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