@inproceedings{zhao-etal-2026-choose,
    title = "Choose Your Lens: Multi-Perspective Value Alignment of Chain-of-Thought Reasoning",
    author = "Zhao, Gejian  and
      Wu, Hanzhou  and
      Zhang, Xinpeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.findings-acl.2075/",
    pages = "41795--41808",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) are increasingly expected to support pluralistic alignment, representing diverse human perspectives. However, current methods often induce motivated reasoning: LLMs tend to hallucinate ``convenient'' facts to forcefully justify a requested stance. To address this, we propose Value-Graph-Consistent Chain-of-Thought (VGC-CoT), a neuro-symbolic framework that enables steerable pluralism without distorting objective reality. We enforce a strict distinction: facts should be shared, while value trade-offs may diverge. Our approach models reasoning as a directed traversal over a multi-perspective graph comprising a fixed factual layer and perspective-specific value layers. By projecting generated CoT paths onto this structure, we align the model with target values while constraining it to a shared factual backbone. Experiments show that our method reduces factual hallucinations by $3\times$ and improves cross-perspective consistency by 25{\%} compared to standard steerable baselines, paving the way for trustworthy pluralistic AI."
}