Get to know the Reranking model technique: A key tool for enterprise information retrieval system
17 Dec 2024
We are now in the age of Large Language Models (LLMs). Since OpenAI released the GPT model, we've seen many applications emerge, such as smart search systems, knowledge management systems, chatbots, and machine translation tools. Each of these applications is powered by LLMs at their core. These applications rely heavily on data, requiring us to find similarities and rank results before processing. While the basic and commonly used method is to find similarity based on cosine similarity between two vectors, today AIGEN will introduce a new method: the reranking model. Before we delve into the reranking model that AIGEN uses to improve the accuracy and performance of many AI services, we need to understand the history of ranking. We'll explore the evolution from traditional full-text search using BM25, to vector search, and finally to reranking models. We'll conclude by comparing the metrics of each method, highlighting their pros and cons. How many types of ranking method? Ranking techniques play a crucial role in information retrieval and natural language processing tasks. Some of the most widely used methods include: Full text search BM25 This is a probabilistic ranking function used to estimate the relevance of documents to a given search query. BM25 (Best Matching 25) is an improvement over earlier models and is widely used in search engines due to its effectiveness and simplicity. Vector Similarity This method represents documents and queries as vectors in a high-dimensional space. The similarity between a document and a query is then computed using metrics like cosine similarity or dot product. This approach is particularly useful when dealing with semantic meaning rather than just keyword matching. Methods like TF-IDF vectorization or more advanced techniques like word embeddings (e.g., Word2Vec, GloVe) are often used to create these vector representations. Reranking method