ANNs are a type of neural network designed to retrieve relevant information from a large dataset. The process involves two phases: data mining and pattern recognition. Data mining helps uncover patterns in the data, while pattern recognition identifies the most suitable match between the query vector and the dataset vectors.
Graph-based ANNS
Graph-based ANNs are now a mainstream solution due to their extensive theoretical and empirical support. These methods involve constructing a graph database that connects the query vector with the dataset vectors based on similarity. The graph database then enables efficient searching of relevant data using various algorithms, such as PageRank or Chebyshev distance.
Traditional ANNS Methods
Several traditional methods have been proposed for ANNs, including nearest neighbor (NN), support vector machine (SVM), and decision trees. These methods rely on the distance between the query vector and the dataset vectors to determine relevance. However, these methods can be computationally expensive and may not perform well with large datasets.
Emergence of Large Language Models
With the growing popularity of Large Language Models (LLMs), ANNs are increasingly being applied to retrieve relevant information from external databases for prompting LLMs. This enhances the reliability of LLMs by providing them with accurate and relevant data.
Conclusion
In conclusion, Active Neural Networks are a crucial tool in information retrieval, enabling efficient and accurate searching of relevant data. With the advent of neural embedding, ANNs can now handle large-scale vectorized datasets and transform discrete data into dense continuous vectors. Graph-based ANNs are now a mainstream solution due to their extensive theoretical and empirical support. Traditional methods such as NN, SVM, and decision trees have been proposed for ANNs, but they can be computationally expensive with large datasets. However, the emergence of LLMs has led to increased applications of ANNs in retrieving relevant information from external databases for prompting LLMs, which enhances their reliability.
