使用 BGE-M3 生成学习型稀疏嵌入_AI阅读总结 — 包阅AI

Sometimes developers need to make a choice when it comes to LLM retrieval approaches. They can use a traditional sparse embedding or a dense embedding. Sparse embeddings work really well for keyword-matching processes. We typically find sparse embeddings in natural language processing (NLP), and these high-dimensional embeddings often contain zero values. The dimensions in these embeddings represent tokens across one (or multiple) language(s). It uses non-zero values to show how relevant each token is to a specific document.

Dense embeddings, on the other hand, are lower-dimensional but they don’t contain any zero values. As the name suggests, dense embeddings are jam-packed with information. This makes dense embeddings ideal for semantic search tasks, making it easier to match the “spirit” of meaning instead of the exact string.

BGE-M3 is a machine learning model used to create an advanced type of embedding called a “learned sparse embedding.” The nice thing about these learned embeddings is that they combine the best of both worlds: the precision of sparse embedding and the semantic richness of dense embeddings. This model uses the tokens in a sparse embedding to learn which other tokens may be relevant or related, even if they’re not explicitly used in the original search string. Ultimately, this yields an embedding that is rich with relevant information.

Meet BERT

Bidirectional Encoder Representations from Transformers (or BERT) is more than meets the eye. It is the underlying architecture that enables advanced machine learning models like BGE-M3 and SPLADE.

BERT approaches text differently than traditional models. Instead of just reading a text string sequentially, it examines everything all at once, taking the relationship between all the components into account. BERT does this with a two-pronged approach. These are separate pre-training tasks that the model implements, but their outputs work together to enrich the meaning of inputs.

1. Masked Language Modeling (MLM): First, BERT randomly hides part of the input token. Then it uses the model to figure out which options make sense for the hidden portions. To do this, it needs to understand the relationship not only between word order but how that order affects meaning.
2. Next Sentence Prediction (NSP): While the MLM works primarily at the sentence level, NSP zooms out further. This task ensures that sentences and paragraphs flow logically, so it learns to predict what makes sense in these broader contexts.

When the BERT model analyzes a query, each layer of the encoder conducts its analysis independently of the other layers. This allows each layer to generate unique results, free from the influence of the other encoders. The output of this is a richer, more robust data set.

It’s important to understand BERT functions because BGE-M3 is based on BERT. The following example demonstrates how BERT works.

BERT in Action

Let’s take a basic query and see how BERT creates an embedding from it:

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The first step is to convert the words in the query string to tokens.

You’ll notice that the model added [CLS] to the beginning and [SEP] to the end of the token. These components simply indicate the beginning and end of a sentence, respectively, at the sentence level.

Next, it needs to convert the tokens into an embedding.

The first part of this process is the embedding. Here, an embedding matrix converts each token into a vector. Next, BERT adds positional embeddings because the order of the words matters and this embedding keeps those relative positions intact. Finally, the segment embedding simply tracks the breaks between sentences.

We can see the embedding output at this point is monochromatic to represent sparse embeddings. To achieve greater density, these embeddings go through multiple encoders. Like the pre-training tasks identified above that work independently of each other, these encoders do the same. The embeddings undergo continual revision as they work through the encoders. The tokens in the sequence provide a critical context for refining the representation generated by each encoder.

Once this process finishes, the final output is a denser embedding than the pre-encoder output. This is especially true when using individual tokens for further processing or tasks that result in a singular, dense representation.

BGE-M3 Enters the Chat

BERT got us dense embeddings, but the goal here is to generate learned sparse embeddings. So now we finally get to the BGE-M3 model.

BGE-M3 is basically an advanced machine learning model that takes BERT further by focusing on enhancing text representation through multifunctionality, multi-linguisticity and multi-granularity. All this is to say that it does more than create dense embeddings by generating learned sparse embeddings that provide the best of both worlds: word meaning and precise word choices.

BGE-M3 in Action

Let’s start with the same query we used to understand BERT. Running the query generates the same sequence of contextualized embeddings that we saw above. We can call this output ( Q ).

The BGE-M3 model goes deeper into these embeddings and attempts to understand the significance of each token on a more granular level. There are several aspects of this.

• Token importance estimation: BGE-M3 doesn’t take the [CLS] token representation Q[0] as the only possible representation. It also evaluates the contextualized embedding of each token Q[i] within the sequence.
• Linear transformation: The model also takes the BERT output and, using a linear layer, creates an importance weighting for each token. We can call the set of weights that BGE-M3 produces W_{lex}.
• Activation function: BGE-M3 then applies a rectified linear unit (ReLU) activation function to the product of W_{lex} and Q[i] to compute the term weight w_{t}for each token. Using ReLU ensures that the term weight is non-negative, contributing to the sparsity of the embedding.
• Learned sparse embedding: The final output result is a sparse embedding where every token has a weighted value that indicates how important it is to the original input string.

BGE-M3 in the Real World

Applying the BGE-M3 model to real-world use cases can help demonstrate the value of this machine learning model. These are areas where organizations stand to benefit from the model’s ability to understand linguistic nuances across large quantities of textual data.

Customer Support Automation — Chatbots and Virtual Assistants

You can use BGE-M3 to power chatbots and virtual assistants, significantly enhancing customer support services. These chatbots can handle a wide range of customer queries, providing instant responses and understanding complex questions and contextual information. They can also learn from interactions to improve over time.

Benefits:

• 24/7 availability: Provides round-the-clock support to customers.
• Cost efficiency: Reduces the need for a large customer support team.
• Improved customer experience: Quick and accurate responses improve customer satisfaction.
• Scalability: Can handle numerous queries simultaneously, ensuring consistent service during peak times.

Content Generation and Management for Marketing and Media

You can leverage BGE-M3 to generate high-quality content for blogs, social media, advertisements and more. It can create articles, social media posts and even full-length reports based on desired tone, style and context. You can also use this model to summarize long documents, create abstracts and generate product descriptions.

Benefits:

• Efficiency: Produces large volumes of content quickly.
• Consistency: Maintains a consistent tone and style across different pieces of content.
• Cost reduction: Lowers the need for large content creation teams.
• Creativity: Helps brainstorm and generate creative content ideas.

Medical Data Analysis — Clinical Documentation and Analysis

Developers in the healthcare sector can use BGE-M3 to analyze clinical documents and patient records, extract relevant information and assist in generating comprehensive medical reports. It can also aid in identifying trends and insights from vast amounts of medical data, supporting better patient care and research.

Benefits:

• Time savings: Reduces the time healthcare professionals spend on documentation.
• Accuracy: Enhances the accuracy of medical records and reports.
• Insight generation: Identifies patterns and trends that can inform better clinical decisions.
• Compliance: Helps ensure documentation complies with regulatory standards.

Conclusion

The BGE-M3 model provides a significant degree of versatility and advanced natural language processing capabilities that have applications across industries and sectors and can provide significant improvements in operational efficiency and service quality.