The Critical Flaw in Dense Embeddings for Retrieval

In the world of Natural Language Processing (NLP), dense embeddings play a crucial role in tasks like information retrieval. However, there is a critical flaw in these dense embeddings that can impact the accuracy of retrieval results. In this video, we will explore what this flaw is and how it can be addressed. To illustrate this issue, let’s consider a simple example.

Understanding Dense Embeddings

Imagine we have three documents, each containing a single sentence. On the right, we have a query. We compute the embeddings of these documents and the query using a chosen embedding model, resulting in a vector for each chunk. We then compare these embeddings to perform similarity search and identify the closest match, which is returned as the result of our retrieval step.

Various embedding models have different dimensions, with some newer models offering larger-dimensional embeddings. The issue with dense embeddings arises when chunks contain extensive information, such as multiple paragraphs, leading to compression of information into a single vector. This compression may result in the loss of crucial details present in the chunk.

The Potential Solution

A potential solution to this problem is proposed in a paper titled “CBER: Effective and Efficient Retrieval via Lightweight Late Interaction.” This approach involves utilizing contextualized late interaction to address the limitations of dense embeddings. By tokenizing documents and queries, computing embeddings for individual tokens, and calculating similarity scores between tokens in queries and documents, this method leverages late interactions to capture more nuanced context.

Each token contributes to the overall similarity score, allowing for a more comprehensive representation of the information present in the chunk. These contextualized embeddings consider the surrounding context of the token, leading to a more robust retrieval process.

Practical Implementation using RoBERTa

Let’s explore a practical example utilizing RoBERTa for semantic search. By training and fine-tuning RoBERTa models using tools like Regi, we can embed and index documents for efficient retrieval. The process involves tokenizing documents, computing embeddings for each token, and creating an index for retrieval purposes.

By querying the index and retrieving relevant documents based on similarity scores, we can observe how RoBERTa’s contextualized embeddings outperform traditional dense embeddings in capturing nuanced context and improving retrieval accuracy.

Comparison with Other Embedding Models

We also compare the performance of RoBERTa with other embedding models, such as OpenAI’s model and open-source embeddings like BGE small English. Through a series of retrieval experiments, we demonstrate how RoBERTa excels in retrieving relevant information and providing contextually rich results.

Conclusion

In conclusion, the flaw in dense embeddings for retrieval can be mitigated by adopting techniques like contextualized late interaction, as demonstrated by RoBERTa. By leveraging advanced NLP models and strategies, we can enhance the accuracy and effectiveness of information retrieval systems.

For those interested in delving deeper into advanced NLP concepts and applications, consider exploring our Advanced NLP course for comprehensive learning opportunities. Thank you for watching, and stay tuned for more insights in our upcoming videos.