Jina's ColBERT v2: Advanced Multilingual Search

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What are ColBERT and post-processing, and why are they so important for search?

ColBERT is a model specifically used for information retrieval. Its name comes from "Contextualized Late Interaction over BERT". It combines the powerful language understanding ability of the BERT model and introduces a novel "late interaction" mechanism on this basis, making search more efficient and accurate.

How does ColBERT work?

Usually in search engines, we need to compare user queries with a large number of documents to find the best matches. Traditional models (such as BERT) combine queries and documents together for processing in the early stages. Although this approach is accurate, it is very computationally intensive, especially when processing large-scale data.

Late interaction is different. Its core idea is to encode the query and document separately first, and then let them "interact" or "compare" in the final stage. The advantage of this is that the encoding of the document can be completed and stored in advance. When a query comes in, only a simple and fast comparison is required, which greatly improves the speed of search.

Differences between ColBERT and ColBERT v2

Original ColBERT: This is the earliest version of the ColBERT model, developed by researchers at Stanford University. Its highlight is the introduction of late interaction for the first time, which has made a major breakthrough in the efficiency of the model.

ColBERTv2: This is an upgraded version of ColBERT. It not only maintains the advantages of late interaction, but also further improves the retrieval effect through some new techniques (such as denoising supervision and residual compression), while reducing the storage requirements of the model.

Why is ColBERT so special?

Efficient retrieval: Traditional search models need to perform a lot of calculations on each possible document when processing queries, while ColBERT can pre-calculate and store the encoding of the document, so only a simple comparison is required during query, which is faster.

Support for large-scale data: Since document encoding can be done in advance, ColBERT is particularly suitable for processing large-scale datasets, such as retrieval tasks with millions or even billions of documents.

Save storage space: ColBERTv2 significantly reduces the storage requirements of the model through compression technology, so that it will not take up too much storage resources when used on large-scale data sets.

Explanation with examples

Suppose you are looking for a book in a library. The traditional method is that every time you look for a book, you must compare the book with your search criteria (such as the title or author) in detail, which is very inefficient. The late interactive method is similar to the library giving each book a short tag (code) in advance. You only need to use the tag to quickly match to find the book you want, which is both accurate and time-saving.

Key points:

The core of the "late interaction" technique is that it does not directly compare the query and the overall vector of the document, but interacts at a more detailed level (such as words or phrases) to find the most relevant matches. This method is often more accurate than traditional methods, especially in complex queries or multilingual environments.

Scenario: Suppose you are using a literature search system and need to find research papers that are highly relevant to a specific topic. Traditional search engines may only be able to search based on keyword matching, but you need more accurate results, such as understanding the semantics and context of the document content.

Jina ColBERT v2 features: Through late interaction technology, Jina ColBERT v2 can perform deeper interactive calculations after encoding queries and documents into vectors to improve retrieval accuracy. This means that even if the keywords in the query do not appear directly in the document, the model can find relevant content based on semantic understanding and rank these documents first.

Summary: Late interaction technology can help search engines handle complex queries more intelligently, especially when the query involves multiple languages or complex content. It can provide more relevant and accurate search results through more sophisticated vector comparisons. ColBERT achieves fast and efficient search results when processing large-scale data through the design of "late interaction". It not only makes important innovations in technology, but also makes practical applications more realistic and feasible. The emergence of this model provides us with faster and smarter search tools, greatly improving the efficiency of information retrieval.

Main Features of ColBERT v2

Excellent retrieval performance: Jina ColBERT v2 has a significant improvement in retrieval performance compared to the original ColBERT-v2 and the previous generation jina-colbert-v1-en, by 6.5% and 5.4% respectively.

Multi-language support: Jina ColBERT v2 supports 89 languages, covering major global languages such as English, Chinese, French, German, and programming languages. By training on corpora in multiple languages, the model performs well in cross-language re-ranking and retrieval tasks. This means that the model is able to process and understand texts from different languages and perform cross-language information retrieval and ranking tasks. This is very important in global application scenarios, such as in a search engine that needs to support multiple languages.

User-controllable output embedding size: Adopts Matryoshka representation learning technology, allowing users to choose different output vector sizes (128, 96, 64 dimensions) to flexibly balance between computational efficiency and retrieval accuracy.

Scenario: Suppose you are building a search system and you need to balance speed and accuracy. For example, in some applications, you want search results to be returned faster, while in other cases, you want the most accurate match possible.

Jina ColBERT v2 features: Allows you to adjust the output vector dimension (e.g. 128, 96, or 64) as needed. Smaller dimensions may slightly reduce accuracy, but will greatly increase processing speed and reduce storage requirements. You can choose the appropriate output dimension according to your specific needs to achieve the best performance.

Cross-language search and re-ranking

Scenario: You have a set of documents in multiple languages, for example some are in French, some are in Spanish, and some are in Japanese. You want to be able to enter an English query and find the most relevant content from these different language documents.

Jina ColBERT v2 features: It can not only retrieve documents in a single language, but also handle cross-language retrieval tasks. For example, when you enter an English query, the model can understand the meaning of the query and find corresponding content in French, Spanish, and Japanese documents, and then return them sorted by relevance.

Significantly reduce storage requirements: By improving the model architecture and training process, Jina ColBERT v2 reduces storage requirements by up to 50% while maintaining high performance, which is particularly important for large-scale information retrieval tasks.

Extended context processing capabilities: The model can process document content with up to 8192 tokens, greatly surpassing the context processing capabilities of many existing models.

Flexible application integration: Jina ColBERT v2 can be embedded and rearranged through the Jina Search Foundation API, supports multiple computing frameworks and platforms, and can be used as a replacement for existing ColBERT models without additional adaptation.

Performance of ColBERT v2:

Retrieval accuracy

Compared to the original ColBERT-v2: Jina ColBERT v2 improves performance by 6.5% in multilingual retrieval tasks. It improves performance by an average of 6.6% in multiple English retrieval tasks. In some specific tasks, such as the LoTTE benchmark, Jina-ColBERT-v2 improves the success rate by 6.1% compared to ColBERTv2.

Compared with the previous generation jina-colbert-v1-en: the performance is improved by 5.4%. This means that under the same query conditions, Jina ColBERT v2 can more accurately find documents related to the query.

Compared with other advanced models: Although in some tests, such as compared with BGE-M3, Jina-ColBERT-v2 is slightly inferior in retrieval tasks in some languages (about 0.66% difference), the advantage of Jina-ColBERT-v2 lies in its smaller embedding dimension and higher storage efficiency, making it more practical in practical applications.

Multi-language support

Support for 89 languages: Jina ColBERT v2 performs well in retrieval tasks in different languages. For example, in multilingual benchmarks such as MIRACL and mMARCO, Jina-ColBERT-v2 has a significant improvement over previous models.

Jina ColBERT v2: Support for 89 languages — Jina ColBERT v2: **Support for 89 languages**

Flexibility in embedding size

Output dimension selection: Jina ColBERT v2 supports output embeddings of dimensions 128, 96, and 64. Under different dimensions, the model's nDCG@10 (normalized discounted cumulative gain of the first 10 results) performance does not change much, with the performance of 128 dimensions being 0.565, 96 dimensions being 0.558, and 64 dimensions being 0.556, showing that embeddings of different sizes can reduce the consumption of computing and storage resources while maintaining high accuracy.

Jina ColBERT v2: supports output embeddings of dimensions 128, 96, and 64

Storage and efficiency

Storage requirements: Jina ColBERT v2 significantly reduces the storage requirements of the model, saving up to 50% of storage space. For example, through the Matryoshka representation learning technology, users can choose a smaller output dimension (such as 64 dimensions), significantly reducing storage and computation costs with only a slight sacrifice in accuracy.

Efficiency and cost: In terms of computing resources, Jina ColBERT v2 can provide higher processing efficiency. Using smaller-dimensional embeddings not only saves storage but also speeds up processing. For example, when reducing a 128-dimensional vector to 64 dimensions, the storage cost can be halved with less than a 1.5% drop in performance.

Specific performance indicators

Performance on English tasks: On the 14 English BEIR benchmark tasks, Jina ColBERT v2 achieves an average score of 0.521, which is higher than Jina ColBERT v1-en’s 0.494 and the original ColBERT v2’s 0.489.

Jina ColBERT v2: Performance on English tasks — Jina ColBERT v2: **Performance on English tasks**

Performance under different output dimensions: When using 128-dimensional vectors, Jina ColBERT v2 has an average nDCG@10 (Normalized Discounted Cumulative Gain) score of 0.565 on the 6 datasets of the BEIR benchmark. When reduced to 96 and 64 dimensions, the scores are 0.558 and 0.556 respectively, with minimal performance degradation.

Official blog: https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/

Technical report: https://arxiv.org/pdf/2408.16672

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