@inproceedings{zhang-etal-2026-locregen,
title = "{L}oc{R}egen: Cost-Efficient Redundancy Removal in Multilingual {E}-commerce Titles with Small Language Models",
author = "Zhang, Bryan and
Walter, Stephan and
Lomanto, Luca and
Arinik, Merve",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.33/",
pages = "528--537",
ISBN = "9789403901411",
abstract = "E-commerce product titles often include redundant information that negatively impacts the user experience. Removing repeated words through restructuring and paraphrasing can make titles more concise and improve readability. While large language models can optimize titles, their computational cost makes them impractical for large-scale applications. In this paper, we first analyze the sources of repetition in multilingual product titles, then present LocRegen, a system that uses smaller language models to efficiently remove redundancies while preserving essential product attributes. Our experiments across five languages show that LocRegen with a 7B model substantially outperforms a 47B mixture-of-experts model: LocRegen achieves a 2.4{\%} redundant title rate compared to 3.5{\%} for the 47B model, and maintains a 3.8{\%} overall error rate across all error categories including key product attribute omission compared to 8.4{\%} for the 47B model. These results demonstrate that LocRegen delivers superior performance on cost-effective hardware with acceptable latency, making it practical for large-scale deployment where much larger models would be computationally prohibitive."
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<abstract>E-commerce product titles often include redundant information that negatively impacts the user experience. Removing repeated words through restructuring and paraphrasing can make titles more concise and improve readability. While large language models can optimize titles, their computational cost makes them impractical for large-scale applications. In this paper, we first analyze the sources of repetition in multilingual product titles, then present LocRegen, a system that uses smaller language models to efficiently remove redundancies while preserving essential product attributes. Our experiments across five languages show that LocRegen with a 7B model substantially outperforms a 47B mixture-of-experts model: LocRegen achieves a 2.4% redundant title rate compared to 3.5% for the 47B model, and maintains a 3.8% overall error rate across all error categories including key product attribute omission compared to 8.4% for the 47B model. These results demonstrate that LocRegen delivers superior performance on cost-effective hardware with acceptable latency, making it practical for large-scale deployment where much larger models would be computationally prohibitive.</abstract>
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%0 Conference Proceedings
%T LocRegen: Cost-Efficient Redundancy Removal in Multilingual E-commerce Titles with Small Language Models
%A Zhang, Bryan
%A Walter, Stephan
%A Lomanto, Luca
%A Arinik, Merve
%Y Shterionov, Dimitar
%Y Vanmassenhove, Eva
%Y De Sisto, Mirella
%Y Blain, Fred
%Y Pourmostafa Roshan Sharami, Javad
%Y Lepp, Lisa
%Y Manna, Chiara
%Y Rescigno, Argentina Anna
%Y Karakanta, Alina
%Y Rigouts Terryn, Ayla
%Y Lardelli, Manuel
%Y Resende, Natalia
%Y Murgolo, Elena
%Y Hackenbuchner, Janiça
%Y Zaretskaya, Anna
%Y Esplà-Gomis, Miquel
%Y Etchegoyhen, Thierry
%Y Gromann, Dagmar
%Y Bawden, Rachel
%Y Haddow, Barry
%Y Szoc, Sara
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, The Netherlands
%@ 9789403901411
%F zhang-etal-2026-locregen
%X E-commerce product titles often include redundant information that negatively impacts the user experience. Removing repeated words through restructuring and paraphrasing can make titles more concise and improve readability. While large language models can optimize titles, their computational cost makes them impractical for large-scale applications. In this paper, we first analyze the sources of repetition in multilingual product titles, then present LocRegen, a system that uses smaller language models to efficiently remove redundancies while preserving essential product attributes. Our experiments across five languages show that LocRegen with a 7B model substantially outperforms a 47B mixture-of-experts model: LocRegen achieves a 2.4% redundant title rate compared to 3.5% for the 47B model, and maintains a 3.8% overall error rate across all error categories including key product attribute omission compared to 8.4% for the 47B model. These results demonstrate that LocRegen delivers superior performance on cost-effective hardware with acceptable latency, making it practical for large-scale deployment where much larger models would be computationally prohibitive.
%U https://aclanthology.org/2026.eamt-1.33/
%P 528-537
Markdown (Informal)
[LocRegen: Cost-Efficient Redundancy Removal in Multilingual E-commerce Titles with Small Language Models](https://aclanthology.org/2026.eamt-1.33/) (Zhang et al., EAMT 2026)
ACL