arXiv:2606.08682v2 Announce Type: replace
Abstract: Activation steering (AS) has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). AS modulate...
By Qi Cao, Jian Lou, Meiting Liu, Wenjie Feng, Dan Li, Anh Tuan Luu
The paper investigates how large language models (LLMs) handle two usage‑based grammatical strategies: coercion, where a mismatched word is reinterpreted to fit its context, and preemption, where a plausible structure is avoided because it never appears in expected contexts. Experiments across models of varying sizes show that larger LLMs successfully recognize and produce constructional productivity in coercive situations, even with novel words, but they fail to exhibit preemptive behavior, lacking the negative evidence that would guide avoidance of unconventional but semantically viable constructions. The authors argue that coercion is detectable due to its surprising, semantically motivated nature, whereas preemption lacks such a signature, leading to an asymmetry where models coerce but do not preempt.
By Claire Bonial, Claire Benet Post, Laura Michaelis, Harish Tayyar Madabushi
arXiv:2606. 08682v1 Announce Type: cross Abstract: Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs).
By Qi Cao, Jian Lou, Meiting Liu, Wenjie Feng, Dan Li, See-Kiong Ng, Anh Tuan Luu
arXiv:2609.35356v2 Announce Type: replace
Abstract: Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted gen...
By Kajetan Dymkiewicz, Tim Farrelly, Adam Prada, Ishaan Panigrahi, Srishti Gureja, Helen Yannakoudakis, Robert Mullins, Victor Gillioz, Daniel Tan, Maxime Rich\'e
arXiv:2605. 00994v2 Announce Type: replace-cross Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors.
By Mohammed Abu Baker, Luca Baroni, Dan Wilhelm
Emergent misalignment (EM) is the phenomenon where fine-tuning a language model on a narrow task leads to harmful behavior in unrelated domains. A leading mechanistic account attributes EM to persona features: latent directions acquired during pre-training that misaligned fine-tuning amplifies.