Deep Delight

Wed, 27 Apr 2016 00:00:00 +0000

Combines Reinforcement Learning with DLITE (Delight) as a new information measure (loss function) for training and fine-tuning of machine/deep learning models.

Library Chat Analytics

Wed, 27 Apr 2016 00:00:00 +0000

Recent advances in machine learning (ML) and large language models (LLMs) are offering exciting opportunities for libraries filled with precious data. This potential shines brightly in areas like chat-based assistance. Yet, there are some roadblocks - concerns about data privacy, the risk of models giving inaccurate information (hallucination), and the expenses related to tailoring and assessing these tools specifically for libraries.

Information Retrieval, often linked to the term ‘Reference Retrieval’, is deeply tied to library studies and services. This approach can help navigate challenges faced by LLMs. It gives us a solid framework to analyze data-driven responses. In this process, librarians also play a pivotal role and their insights and feedback are vital in ensuring that this blend of traditional library methods and cutting-edge AI truly benefits library administrators and users.

Deep Learning on Weimao Ke

Deep Delight

Library Chat Analytics