---
title: "Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering"
authors:
  - "Chendi Wang"
  - "Liam Cunningham"
  - "Tom Yishay"
  - "Jieying Chen"
doi: "10.24963/ijcai.2026/831"
url: "https://ijcai.org/proceedings/2026/831"
pdf: "https://ijcai.org/proceedings/2026/0831.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7474-7482"
track: "AI and Social Good"
keywords:
  - "Uncertainty in AI: Uncertainty in AI"
  - "Natural Language Processing: Natural Language Processing"
  - "Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"
  - "AI Ethics, Trust, Fairness: AI Ethics, Trust, Fairness"
---

# Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering

Chendi Wang, Liam Cunningham, Tom Yishay, Jieying Chen

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7474-7482. DOI: [10.24963/ijcai.2026/831](https://doi.org/10.24963/ijcai.2026/831)

## Abstract

Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings where factual errors and ideological distortions are high-stakes. We present a reproducible measurement framework that treats hallucinations, unsupported statements in document-grounded QA, as diagnostic signals of ideological drift. Using 21,727 expert-labeled U.S. political news articles from QBias spanning left, center, and right sources, we (i) generate an article-specific question, (ii) elicit document-grounded answers from three open-weight LLMs and one proprietary model, (iii) detect sentence-level hallucinations via reference-based comparison, (iv) classify the ideological valence of hallucinated sentences with a fine-tuned stance classifier, and (v) probe output logits to relate token-level uncertainty to hallucination and drift. Hallucination rates vary substantially across models and concentrate in contentious topics, while source-ideology differences in hallucination frequency are modest. In contrast, hallucination content exhibits robust leftward drift: a majority of hallucinated sentences are classified as left-leaning, including among hallucinations generated from right-leaning sources. Logit-level analysis shows hallucinations arise in high-entropy generation contexts, and in some models uncertainty also predicts leftward drift, consistent with an "uncertainty → guessing" mechanism. We discuss implications for auditing AI-mediated political information and for designing safeguards in election-relevant deployments.