--- language: - it license: cc-by-nc-4.0 task_categories: - text-retrieval task_ids: - document-retrieval pretty_name: IT-RAG-Bench size_categories: - 1K **Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems** > Stefano Cirillo, Domenico Desiato, Giuseppe Polese, Giandomenico Solimando — arXiv 2026 > 📄 [https://arxiv.org/abs/2605.23618](https://arxiv.org/abs/2605.23618) > 💻 [https://github.com/cciro94/GoogleEmbeddings2-benchmark](https://github.com/cciro94/GoogleEmbeddings2-benchmark) --- ## Dataset Summary IT-RAG-Bench provides **3,200 Italian passages** and **640 natural-language queries** spanning three document styles representative of real Italian information retrieval scenarios: - **Encyclopedic** passages (Wikipedia-style) - **FAQ** passages (public-administration question–answer pairs) - **Legal/regulatory** article excerpts (Italian legislative style) The dataset was generated synthetically with a fixed random seed (42) using Italian-language topic vocabularies drawn from AI/NLP, legal, and public-administration domains, ensuring full reproducibility without relying on crawled or licensed data. --- ## Dataset Structure ### Configurations | Config | File | Rows | Description | |--------|------|------|-------------| | `corpus` | `data/corpus.jsonl` | 3,200 | Passages to retrieve from | | `queries` | `data/queries.jsonl` | 640 | Italian natural-language queries | | `qrels` | `data/qrels.jsonl` | 1,246 | Query–document relevance judgements | ### Corpus Composition | Type | Count | Style description | |------|-------|-------------------| | `wiki` | 1,200 | Encyclopedic passages about AI, NLP, and Italian regulatory topics | | `faq` | 800 | Public-administration FAQ: question + structured answer | | `legal` | 1,200 | Synthetic Italian legal articles (Art. N format) | **Total passages:** 3,200 | **Total queries:** 640 | **Total relevance pairs:** 1,246 ### Field Schema **`corpus.jsonl`** ```json { "_id": "wiki_it_0", "text": "Il documento esamina intelligenza artificiale nel contesto di edilizia ...", "type": "wiki" } ``` **`queries.jsonl`** ```json { "_id": "q_it_0", "text": "Quali sono i risultati dell'uso di intelligenza artificiale nel settore sanità?" } ``` **`qrels.jsonl`** ```json { "query_id": "q_it_0", "corpus_id": "wiki_it_42", "score": 1 } ``` Relevance scores are binary (0 / 1). Each query has between 1 and 3 relevant documents. --- ## Dataset Creation ### Motivation Existing Italian retrieval benchmarks are scarce and often require special licensing. IT-RAG-Bench was created to provide a freely available, reproducible Italian evaluation set for comparing embedding models across typical Italian enterprise retrieval scenarios (administrative portals, legal databases, public FAQs). ### Generation Process The corpus is generated from parameterised templates filled with Italian-language vocabulary lists: - **Topics:** intelligenza artificiale, reti neurali, recupero dell'informazione, elaborazione del linguaggio naturale, contratti digitali, normativa GDPR, diritto del lavoro, appalti pubblici, tutela ambientale, previdenza sociale, proprietà intellettuale, riforma fiscale - **Sectors:** pubblica amministrazione, sanità, istruzione, finanza, edilizia, agricoltura, trasporti, commercio elettronico, sicurezza informatica, energia rinnovabile Queries are generated from six Italian question templates (e.g., *"Quali sono i risultati dell'uso di {topic} nel settore {sector}?"*). Relevance labels are assigned by randomly associating each query with 1–3 documents of a randomly selected type. **Random seed:** 42 (fully deterministic and reproducible) ### Caveats Because relevance labels are randomly assigned rather than annotated by humans, absolute metric scores are lower than on human-annotated benchmarks. The dataset is best used for **comparative evaluation** (ranking models against each other) rather than measuring absolute retrieval performance. --- ## Benchmark Results The following results were obtained in the companion paper using FAISS HNSW indexing and nDCG@10 as the primary metric (640 queries): | Model | Type | Recall@1 | Recall@5 | Recall@10 | MRR | nDCG@10 | |-------|------|----------|----------|-----------|-----|---------| | **GE2** (Google Embeddings 2) | API | 0.061 | 0.288 | 0.476 | 0.259 | **0.282** | | **mE5** (multilingual-e5-large) | Open | 0.051 | 0.280 | 0.489 | 0.243 | 0.279 | | **E5-large** | Open | 0.053 | 0.279 | 0.439 | 0.247 | 0.262 | | **mpnet** | Open | 0.054 | 0.238 | 0.397 | 0.240 | 0.243 | | **BGE-M3** | Open | 0.046 | 0.253 | 0.404 | 0.224 | 0.238 | | **LaBSE** | Open | 0.048 | 0.190 | 0.315 | 0.184 | 0.189 | ### Experimental Setup - **Index:** FAISS HNSW (M=32, efConstruction=200, efSearch=128) - **Embedding cache:** SHA-256 keyed disk cache (diskcache), preventing redundant API calls - **Reproducibility:** fixed seed 42, deterministic CUDA operations - **Hardware:** NVIDIA A100 40GB (open-source models), API calls for commercial models --- ## Usage ```python from datasets import load_dataset # Load corpus, queries, and relevance judgements corpus = load_dataset("Siando/it-rag-bench", "corpus", split="train") queries = load_dataset("Siando/it-rag-bench", "queries", split="train") qrels = load_dataset("Siando/it-rag-bench", "qrels", split="train") # Example: build a corpus dict corpus_dict = {row["_id"]: row["text"] for row in corpus} ``` ### Minimal retrieval evaluation example ```python from datasets import load_dataset corpus = {r["_id"]: r["text"] for r in load_dataset("Siando/it-rag-bench", "corpus", split="train")} queries = {r["_id"]: r["text"] for r in load_dataset("Siando/it-rag-bench", "queries", split="train")} qrels = {} for r in load_dataset("Siando/it-rag-bench", "qrels", split="train"): qrels.setdefault(r["query_id"], {})[r["corpus_id"]] = r["score"] ``` --- ## Repository & Code The full benchmarking framework (embedding clients, FAISS retrieval, chunking ablations, plotting) is available at: **[https://github.com/cciro94/GoogleEmbeddings2-benchmark](https://github.com/cciro94/GoogleEmbeddings2-benchmark)** --- ## Citation If you use IT-RAG-Bench in your research, please cite: ```bibtex @misc{cirillo2026benchmarkinggoogleembeddings2, title = {Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems}, author = {Stefano Cirillo and Domenico Desiato and Giuseppe Polese and Giandomenico Solimando}, year = {2026}, eprint = {2605.23618}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, url = {https://arxiv.org/abs/2605.23618} } ``` --- ## License This dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. You are free to use and adapt it for non-commercial purposes with proper attribution. See [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) for details.