@inproceedings{ghorbanpour-etal-2026-latent,
title = "Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops",
author = "Ghorbanpour, Faeze and
Fierro, Constanza and
Fraser, Alexander and
S{\o}gaard, Anders",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1206/",
pages = "25464--25475",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space intervention can reduce this inconsistency. We train layer-specific autoencoders on parallel multilingual representations and apply inference-time corrections to factual QA prompts. We find that latent intervention improves geometric alignment between languages, and that this improvement translates into consistent gains in cross-lingual consistency with English across both open-ended and multiple-choice QA formats, without degrading factual accuracy. In open-ended QA, Spearman{'}s rank correlation between English and non-English languages improves substantially, with gains of 0.16 for English-Arabic and 0.20 for English-Russian pairs. In multiple-choice QA, answer agreement with English improves consistently across both KLAR and mParaRel. Ablations show that AE reconstruction drives these gains, whereas PCA-based and mean-shift subspace projections contribute only marginally to the observed improvements in consistency."
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<abstract>Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space intervention can reduce this inconsistency. We train layer-specific autoencoders on parallel multilingual representations and apply inference-time corrections to factual QA prompts. We find that latent intervention improves geometric alignment between languages, and that this improvement translates into consistent gains in cross-lingual consistency with English across both open-ended and multiple-choice QA formats, without degrading factual accuracy. In open-ended QA, Spearman’s rank correlation between English and non-English languages improves substantially, with gains of 0.16 for English-Arabic and 0.20 for English-Russian pairs. In multiple-choice QA, answer agreement with English improves consistently across both KLAR and mParaRel. Ablations show that AE reconstruction drives these gains, whereas PCA-based and mean-shift subspace projections contribute only marginally to the observed improvements in consistency.</abstract>
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%0 Conference Proceedings
%T Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops
%A Ghorbanpour, Faeze
%A Fierro, Constanza
%A Fraser, Alexander
%A Søgaard, Anders
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ghorbanpour-etal-2026-latent
%X Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space intervention can reduce this inconsistency. We train layer-specific autoencoders on parallel multilingual representations and apply inference-time corrections to factual QA prompts. We find that latent intervention improves geometric alignment between languages, and that this improvement translates into consistent gains in cross-lingual consistency with English across both open-ended and multiple-choice QA formats, without degrading factual accuracy. In open-ended QA, Spearman’s rank correlation between English and non-English languages improves substantially, with gains of 0.16 for English-Arabic and 0.20 for English-Russian pairs. In multiple-choice QA, answer agreement with English improves consistently across both KLAR and mParaRel. Ablations show that AE reconstruction drives these gains, whereas PCA-based and mean-shift subspace projections contribute only marginally to the observed improvements in consistency.
%U https://aclanthology.org/2026.findings-emnlp.1206/
%P 25464-25475
Markdown (Informal)
[Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops](https://aclanthology.org/2026.findings-emnlp.1206/) (Ghorbanpour et al., Findings 2026)
ACL