Incremental Multilingual Text2Cypher with Adapter Combination

TL;DR

This paper presents an efficient incremental multilingual Text2Cypher system using adapter fusion techniques, enabling language expansion with minimal retraining and maintaining high accuracy.

Contribution

It introduces a novel adapter fusion method that supports incremental language addition without full re-training, outperforming linear merging methods.

Findings

Fusion MLP recovers 75% of joint fine-tuning accuracy

Outperforms linear merging across all three languages

Enables incremental language expansion with minimal data and retraining

Abstract

Large Language Models enable users to access database using natural language interfaces using tools like Text2SQL, Text2SPARQL, and Text2Cypher, which translate user questions into structured database queries. While these systems improve database accessibility, most research focuses on English with limited multilingual support. This work investigates a scalable multilingual Text2Cypher, aiming to support new languages without re-running full fine-tuning, avoiding manual hyper-parameter tuning, and maintaining performance close to joint multilingual fine-tuning. We train language-specific LoRA adapters for English, Spanish, and Turkish and combined them via uniform linear merging or learned fusion MLP with dynamic gating. Experimental results show that the fusion MLP recovers around 75\% of the accuracy gains from joint multilingual fine-tuning while requiring only a smaller subset of the data, outperforming linear merging across all three languages. This approach enables incremental language expansion to new languages by requiring only one LoRA adapter and a lightweight MLP retraining. Learned adapter fusion offers a practical alternative to expensive joint fine-tuning, balancing performance, data efficiency, and scalability for multilingual Text2Cypher task.