Parameter-Efficient Tuning vs. Full Fine-Tuning in Cross-Lingual Dense Retrieval for Low-Resource Languages

Abstract

Abstract: Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task. However, cross-lingual data for training is often scarcely available. In this paper, rather than using more cross-lingual data for training, we propose to use cross-lingual query generation to augment passage representations with queries in languages other than the original passage language. These augmented representations are used at inference time so that the representation can enco

Research Question

What is the impact of parameter-efficient tuning methods versus full fine-tuning on cross-lingual dense retrieval robustness for unseen low-resource languages?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has passed Gate 2: a Lean4 proof source type-checks, or a sealed-sandbox run reproduced the reported results within the stated tolerance. A reproducible artifact (proof source or repro script and results) is attached to this record. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance