Practical Solutions and Value of OneGen: An AI Framework
Challenges in Current Deployment of Large Language Models (LLMs)
A major challenge in the current deployment of Large Language Models (LLMs) is their inability to efficiently manage tasks that require both generation and retrieval of information. LLMs excel at generating coherent and contextually relevant text but struggle to handle retrieval tasks, increasing computational complexity, inference time, and the risk of error, especially in multi-turn dialogues or complex reasoning scenarios.
OneGen’s Unified Retrieval and Generation
OneGen is a novel solution that unifies the retrieval and generation processes into a single forward pass within an LLM. By integrating autoregressive retrieval tokens into the model, OneGen enables the system to handle both tasks simultaneously without the need for multiple forward passes or separate retrieval and generation models. This innovative approach significantly reduces computational overhead and inference time, enhancing the efficiency of LLMs.
Technical Foundation and Performance
The technical foundation of OneGen involves augmenting the standard LLM vocabulary with retrieval tokens, and it has been evaluated on several datasets, demonstrating superior performance in various tasks requiring both retrieval and generation when compared to existing models. It achieved notable improvements in accuracy and F1 scores, particularly in multi-hop question-answering and entity-linking tasks.
Revolutionizing LLMs for Real-World Applications
OneGen introduces an efficient, one-pass solution to the challenge of integrating retrieval and generation within LLMs. By leveraging retrieval tokens and utilizing contrastive learning, it overcomes the inefficiencies and complexities of previous methods, making LLMs more applicable to real-world, high-speed, and high-accuracy applications.
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