Improving Zero-Shot Cross-Lingual Retrieval Robustness via Artificial Code-Switched Training
Abstract
Abstract: Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train ranking models on artificially code-switched data instead, which we generate by utilizing bilingual lexicons. To this end, we experiment with lexicons induced from (1) cross-lingual word embeddings and (2) parallel Wikipedia page titles. We use
Research Question
Does training on artificially code-switched datasets improve the robustness of zero-shot cross-lingual retrievers against query-document language mismatches compared to standard monolingual training on high-resource languages?
Verification Level
| Paper level | L2, Source-grounded claims | |
| Source-grounded claims | 16 | |
| Claim record source | parsed source sections |
Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.
Truth-Engine Gate Verdict
| Status | Falsified | |
| Gate | Gate 2 — Verification (formal proof or sandbox reproduction) | |
| Reason | [Gate 3 RED-TEAM FALSIFIED] avg_attack_score=5.7/10. COUNTEREXAMPLE_HUNTER(0.0):Attacker error: Unterminated string starting at: line 1 column 33 (char 32); CITATION_AUDITOR(8.5):The verification record shows a computed value of 14.9 against an expected 15.3,; REPLICATION_ATTACKER(8.5):The verification script computes a trivial derived quantity (sum of absolute MRR | |
| Evaluated | 2026-06-19T13:06:40.773204+00:00 |
A claim in this record was tested against Gate 2 and failed: a counterexample was found, a proof did not type-check, or a reproduction attempt did not match the reported results. Evidence for the failure 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
| Tier | Flagship candidate | |
| Basis | Review score, verified-claim count, and public artifact coverage meet flagship-candidate thresholds. |
Descriptive public triage only; this tier does not alter current publication or DOI behavior.
Quality Dimensions
| Evidence strength | MEDIUM | |
| Citation grounding | MEDIUM | |
| Uncertainty disclosure | MEDIUM | |
| Reproducibility status | HIGH |
Automated triage signals derived from public fields; not human peer review or independent validation.
Correction Record
| Status | CURRENT |
| Correction count | 0 |
| Manifest contract | paper-manifest-v1.1 |
| Correction contract | correction-record-v1 |
Public corrections are additive records. Current status does not claim the synthesis is error-free.
Provenance
| Publisher | Assignee Research |
| Public provenance | L4, External archival record |
| Report artifact | Available |
| External record | Registered |
| Claim lineage | 16 aggregate source-grounded claims |
| Review method | Automated multi-reviewer assessment |
| Quality guide | How to read scores, claims, manifests, and evidence links |
| Provenance contract | source-provenance-v1 |
| Note | Machine-generated synthesis of existing literature. Not primary research. |