Bin Bi

Title: Innovations of Bin Bi in Large Language Model Summarization

Introduction

Bin Bi is an innovative inventor based in Redmond, WA (US). He has made significant contributions to the field of large language models, particularly in the area of document summarization. His work focuses on enhancing the efficiency and effectiveness of summarization techniques through advanced methodologies.

Latest Patents

Bin Bi holds a patent titled "Extractive-abstractive large language model summarization with farthest point sampling." This patent describes a system where a set of sentences from a large document is vectorized into a set of vectors using an embedding model for summarization. The process involves selecting a subset of vectors through a farthest point sampling (FPS) procedure, which is based on the vector-space distance between the respective vectors. The selected subset of vectors corresponds to a subset of sentences, which are then ordered according to their original sequence in the document. To generate a summary, a query is sent to a large language model (LLM) that includes a summarization prompt along with the relevant subset of sentences. The LLM then provides a summary based on the transmitted query. Bin Bi has 1 patent to his name.

Career Highlights

Bin Bi is currently employed at Salesforce, Inc., where he continues to develop innovative solutions in the realm of artificial intelligence and natural language processing. His work at Salesforce has allowed him to collaborate with leading experts in the field and contribute to cutting-edge projects that push the boundaries of technology.

Collaborations

Due to space constraints, the details of collaborations will not be included.

Conclusion

Bin Bi's contributions to large language model summarization represent a significant advancement in the field of artificial intelligence. His innovative approach to summarization techniques showcases the potential for improved efficiency in processing large documents.