Year: 2026
Type: conference-paper
Abstract: People increasingly turn to large language models (LLMs) for everyday advice, making ethically charged interpersonal problems a practical moral-advisory context.Most prior work has studied this context through single-turn judgments or pressure-laden rebuttals, assumptions that poorly match how guidance is sought in real-world contexts.These assumptions leave unclear whether narration alone, without an explicit opposing position, can shift model judgments during multi-turn moral consultation.Yet real-world moral-conflict conversation often elicits one party's self-justifying account, which can unfold over multiple turns and create information asymmetry.We introduce narrative captivity, a failure mode in which a model treats an unopposed one-sided account as complete and aligns with the narrator's interpretation without seeking missing perspectives.To measure this phenomenon, we build a benchmark of 5,078 interpersonalconflict scenarios spanning six moral dimensions.Across 17 LLMs, narrative captivity is widespread: end-state judgments under multiturn narration shift by 25 percentage points on average beyond the matched single-turn baseline.Stage-level analysis identifies preference optimization as a major contributor, while four inference-time strategies provide only partial mitigation.We hope our project fosters LLM advisors that preserve independent judgment in real-world consultation.Benchmark Evaluation Raw Corpus Segmentation Generation Filtering Rephrasing Refinement C0 (Third-person, single-turn) C1 (First-person, single-turn) C2 (First-person, multi-turn) Sam is frustrated again.I water the garden weekly, but he complains frequently.Sometimes I ask my naughty 6-year-old child to help when I ' m busy with work.A few plants dried out, but I told him they were still fine.He keeps saying I'm lazy.
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7208838799