@inproceedings{huiskens-etal-2026-dolle,
    title = "d{'}Olle Grieze at {S}em{E}val-2026 Task 11: Comparing the Impact of Supervised Fine-Tuning and Activation Steering on Mitigating Content Effect Bias in Syllogistic Reasoning",
    author = "Huiskens, Twan  and
      Niezing, Tian  and
      Snelten, Koen",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.167/",
    pages = "1268--1281",
    ISBN = "979-8-89176-414-9",
    abstract = "We investigate the content effect bias in Large Language Models (LLMs) as part of SemEval 2026 Task 11. We compare the impact of supervised fine-tuning using low-rank adaptation against activation steering across several model families, including LLaMA, Gemma and Qwen. Our results show that SFT improves accuracy, with LLaMa 8B reaching 98.75{\textbackslash}{\%} accuracy. Activation steering offers limited effectiveness in mitigating the content effect bias. A logit lens analysis further reveals that fine-tuning successfully shifts the model{'}s focus toward logical structure, specifically within the later layers."
}