@inproceedings{lu-etal-2026-relational,
title = "Relational Linearity is a Predictor of Hallucinations",
author = "Lu, Yuetian and
Liu, Yihong and
Gerstner, Sebastian and
Hirlimann, Lea and
Rohweder, Jonas and
Schuetze, Hinrich",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.405/",
pages = "9327--9345",
ISBN = "979-8-89176-459-0",
abstract = "Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: ``Which instrument did Glenn Gould play?'', but we ask these questions for synthetic entities designed to be unknown to the model. We find that LMs like Gemma-7B-IT frequently hallucinate, i.e., they have difficulty recognizing that the hallucinated fact is not part of their knowledge. Based on the idea of linear relational embeddings, we put forward the following hypothesis. (i) Due to the abstract scheme that is used to represent them, LMs can easily produce plausible objects for non-existing subjects of linear relations, which can lead to hallucinations. (ii) For nonlinear relations, this mechanism for producing an object is not available and so a hallucination is easier to avoid. To test this hypothesis, we create SyntHal, a synthetic unknown-entity benchmark for 15 relations. We find that across four instruction-tuned models, relational linearity is a strong predictor of models hallucinating an object for an unknown subject vs refusing to give an answer, with correlations $r \in [.58, .84]\$. While this is not direct evidence for the hypothesized causal mechanism, it is suggestive and opens up a new line of inquiry into understanding LM hallucinations.$"
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<abstract>Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: “Which instrument did Glenn Gould play?”, but we ask these questions for synthetic entities designed to be unknown to the model. We find that LMs like Gemma-7B-IT frequently hallucinate, i.e., they have difficulty recognizing that the hallucinated fact is not part of their knowledge. Based on the idea of linear relational embeddings, we put forward the following hypothesis. (i) Due to the abstract scheme that is used to represent them, LMs can easily produce plausible objects for non-existing subjects of linear relations, which can lead to hallucinations. (ii) For nonlinear relations, this mechanism for producing an object is not available and so a hallucination is easier to avoid. To test this hypothesis, we create SyntHal, a synthetic unknown-entity benchmark for 15 relations. We find that across four instruction-tuned models, relational linearity is a strong predictor of models hallucinating an object for an unknown subject vs refusing to give an answer, with correlations r ın [.58, .84]$. While this is not direct evidence for the hypothesized causal mechanism, it is suggestive and opens up a new line of inquiry into understanding LM hallucinations.</abstract>
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%0 Conference Proceedings
%T Relational Linearity is a Predictor of Hallucinations
%A Lu, Yuetian
%A Liu, Yihong
%A Gerstner, Sebastian
%A Hirlimann, Lea
%A Rohweder, Jonas
%A Schuetze, Hinrich
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lu-etal-2026-relational
%X Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: “Which instrument did Glenn Gould play?”, but we ask these questions for synthetic entities designed to be unknown to the model. We find that LMs like Gemma-7B-IT frequently hallucinate, i.e., they have difficulty recognizing that the hallucinated fact is not part of their knowledge. Based on the idea of linear relational embeddings, we put forward the following hypothesis. (i) Due to the abstract scheme that is used to represent them, LMs can easily produce plausible objects for non-existing subjects of linear relations, which can lead to hallucinations. (ii) For nonlinear relations, this mechanism for producing an object is not available and so a hallucination is easier to avoid. To test this hypothesis, we create SyntHal, a synthetic unknown-entity benchmark for 15 relations. We find that across four instruction-tuned models, relational linearity is a strong predictor of models hallucinating an object for an unknown subject vs refusing to give an answer, with correlations r ın [.58, .84]$. While this is not direct evidence for the hypothesized causal mechanism, it is suggestive and opens up a new line of inquiry into understanding LM hallucinations.
%U https://aclanthology.org/2026.emnlp-main.405/
%P 9327-9345
Markdown (Informal)
[Relational Linearity is a Predictor of Hallucinations](https://aclanthology.org/2026.emnlp-main.405/) (Lu et al., EMNLP 2026)
ACL
- Yuetian Lu, Yihong Liu, Sebastian Gerstner, Lea Hirlimann, Jonas Rohweder, and Hinrich Schuetze. 2026. Relational Linearity is a Predictor of Hallucinations. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9327–9345, Budapest, Hungary. Association for Computational Linguistics.