Cross-Lingual Cross-Modal Retrieval With Noise-Robust Fine-Tuning (IEEE 2024)
This entry summarizes an IEEE 2024 paper indexed in the Multi-Lingual section of a curated information-retrieval reading list.
Metadata
| Field | Content | |-------|---------| | Title | Cross-Lingual Cross-Modal Retrieval With Noise-Robust Fine-Tuning | | Venue | IEEE, 2024 | | Source | IEEE Xplore document 10530137 | | Resource type | Academic paper | | Section | Multi-Lingual |
Problem and Motivation
The paper targets cross-lingual cross-modal retrieval: retrieving images or other modalities using queries written in languages different from those seen during pre-training. This setting is challenging because:
- Training pairs across languages and modalities are often noisy or weakly aligned;
- Cross-lingual semantic gaps compound with cross-modal gaps;
- Standard fine-tuning can overfit to mislabeled or mismatched pairs.
- CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents (arXiv 2504.16264)
- CL2CM: Improving cross-lingual cross-modal retrieval via cross-lingual contrastive learning
- Evaluating Large Language Models for Cross-Lingual Retrieval (arXiv 2509.14749)
- Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval (MM 2024, DOI 10.1145/3664647.3680886)
- The Cross-Lingual Cost: Retrieval Biases in RAG over Arabic-English Corpora (arXiv 2507.07543)
- Researchers: reproduce the core comparisons and check whether statistical significance and compute cost are reported;
- Engineers: extract pluggable modules (encoders, rerankers) and assess integration cost with existing stacks;
- Product owners: prioritize user-perceivable benefits (latency, answer trustworthiness) over offline nDCG alone.
The proposed solution is a noise-robust fine-tuning strategy that improves retrieval robustness when adaptation data contains label or alignment noise.
Context Within the Field
The forum post situates the work along the evolution of neural IR:
1. Sparse lexical retrieval (BM25) → dense bi-encoder retrieval → cross-encoder reranking; 2. Late-interaction and generative retrieval; 3. Retrieval-augmented generation (RAG) and agentic search, where retrieval strategy itself becomes a learnable, iterative decision process.
In this landscape, noise-robust fine-tuning addresses the data-quality bottleneck that limits multilingual multimodal retrieval deployment.
Engineering Checklist (from the post)
| Concern | Recommendation | |---------|----------------| | Data | Partition indexes, sanitize PII, version embeddings with rollback | | Latency | Cascade retrieval with early stopping, cache hot queries, async rerank | | Quality | Validate offline gains via interleaving experiments and human audits | | Safety | Source whitelisting, adversarial detection, output filtering | | Cost | Route to smaller models, distillation, hybrid sparse+dense retrieval |