@inproceedings{tumurchuluun-etal-2025-tenseloc,
title = "{T}ense{L}o{C}: Tense Localization and Control in a Multilingual {LLM}",
author = "Tumurchuluun, Ariun-Erdene and
Al Ghussin, Yusser and
Mare{\v{c}}ek, David and
Genabith, Josef Van and
Dutta Chowdhury, Koel",
editor = "Adelani, David Ifeoluwa and
Arnett, Catherine and
Ataman, Duygu and
Chang, Tyler A. and
Gonen, Hila and
Raja, Rahul and
Schmidt, Fabian and
Stap, David and
Wang, Jiayi",
booktitle = "Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)",
month = nov,
year = "2025",
address = "Suzhuo, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.mrl-main.17/",
pages = "243--264",
ISBN = "979-8-89176-345-6",
abstract = "Multilingual language models excel across languages, yet how they internally encode grammatical tense remains largely unclear. We investigate how decoder-only transformers represent, transfer, and control tense across eight typologically diverse languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. We construct a synthetic tense-annotated dataset and combine probing, causal analysis, feature disentanglement, and model steering to LLaMA-3.1 8B. We show that tense emerges as a distinct signal from early layers and transfers most strongly within the same language family. Causal tracing reveals that attention outputs around layer 16 consistently carry cross-lingually transferable tense information. Leveraging sparse autoencoders in this subspace, we isolate and steer English tense-related features, improving target-tense prediction accuracy by up to 11{\%}{\%} in a downstream cloze task."
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%0 Conference Proceedings
%T TenseLoC: Tense Localization and Control in a Multilingual LLM
%A Tumurchuluun, Ariun-Erdene
%A Al Ghussin, Yusser
%A Mareček, David
%A Genabith, Josef Van
%A Dutta Chowdhury, Koel
%Y Adelani, David Ifeoluwa
%Y Arnett, Catherine
%Y Ataman, Duygu
%Y Chang, Tyler A.
%Y Gonen, Hila
%Y Raja, Rahul
%Y Schmidt, Fabian
%Y Stap, David
%Y Wang, Jiayi
%S Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhuo, China
%@ 979-8-89176-345-6
%F tumurchuluun-etal-2025-tenseloc
%X Multilingual language models excel across languages, yet how they internally encode grammatical tense remains largely unclear. We investigate how decoder-only transformers represent, transfer, and control tense across eight typologically diverse languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. We construct a synthetic tense-annotated dataset and combine probing, causal analysis, feature disentanglement, and model steering to LLaMA-3.1 8B. We show that tense emerges as a distinct signal from early layers and transfers most strongly within the same language family. Causal tracing reveals that attention outputs around layer 16 consistently carry cross-lingually transferable tense information. Leveraging sparse autoencoders in this subspace, we isolate and steer English tense-related features, improving target-tense prediction accuracy by up to 11%% in a downstream cloze task.
%U https://aclanthology.org/2025.mrl-main.17/
%P 243-264
Markdown (Informal)
[TenseLoC: Tense Localization and Control in a Multilingual LLM](https://aclanthology.org/2025.mrl-main.17/) (Tumurchuluun et al., MRL 2025)
ACL
- Ariun-Erdene Tumurchuluun, Yusser Al Ghussin, David Mareček, Josef Van Genabith, and Koel Dutta Chowdhury. 2025. TenseLoC: Tense Localization and Control in a Multilingual LLM. In Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025), pages 243–264, Suzhuo, China. Association for Computational Linguistics.