Click here to download project base paper of tools ai project.
Abstract:
Large Language Model (LLM) based Generative tools ai systems have seen significant progress in recent years. Integrating a knowledge retrieval architecture allows for seamless integration of private data into publicly available Generative AI systems using pre-trained LLM without requiring additional model fine-tuning. Moreover, the Retrieval-Centric Generation (RCG) approach, a promising future research direction that explicitly separates the roles of LLMs and retrievers in context interpretation and knowledge memorization, potentially leads to more efficient implementation. SimplyRetrieve is an open-source tool to provides a localized, lightweight, and user-friendly interface to these sophisticated advancements to the machine learning community. SimplyRetrieve features a GUI and API-based RCG platform, assisted by a Private Knowledge Base Constructor and a Retrieval Tuning Module. By leveraging these capabilities, users can explore the potential of RCG for improving generative AI performance while maintaining privacy standards.
Training language models (LLMs)requires immense computational resources, often involving thousands of high-end GPUs. Fine-tuning these models can also be challenging. Although prompt engineering helped to reduce the need for fine-tuning, there was still noticeable instruction misalignment when interacting with a human user. To address this issue, techniques such as reinforcement learning from human feedback(RLHF) (Christianoetal.,2017)have been explored to align thebehaviorofLLMswithhumanvalues. The tool is available at https://github.com/RCGAI/SimplyRetrieve with an MIT license.
