Retrieve Anything To Augment Large Language Models
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Copyright Continuum Labs - 2023
Last updated
This October 2023 paper introduces LLM-Embedder, a unified embedding model designed to support the diverse retrieval augmentation needs of Large Language Models (LLMs).
The authors aim to overcome the inherent limitations of LLMs in terms of knowledge, memory, and capability boundaries by connecting them with external assistance through retrieval augmentation.
LLMs face challenges due to their inherent limitations in knowledge, memory, and capability.
External assistance is sought through retrieval augmentation to overcome these limitations.
Retrievers play a crucial role in connecting LLMs with external components, such as knowledge bases, memory stores, and tool-benches.
LLM-Embedder is a unified embedding model designed to comprehensively support the diverse retrieval augmentation needs of LLMs.
It aims to bridge the gap between LLMs and the external world by providing a versatile solution for various retrieval tasks.
The unified model offers advantages in terms of streamlined system management, enhanced operational efficiency, and potential benefits from composite data across different scenarios.
The authors curate a diverse training dataset comprising tasks closely related to retrieval augmentation for LLMs.
The dataset includes question answering, conversational search, tool learning, instruction tuning, and generation tasks.
The datasets are categorised as labeled data (with hard-coded labels) and non-labeled data (without explicit labels).
The authors optimise the training methodology to address the challenges of training a unified model for diverse retrieval tasks.
They formulate training rewards based on a combination of hard labels from original datasets and soft rewards obtained from LLM's output.
Stabilised distillation is introduced to mitigate the issue of fluctuating LLM output scores by jointly incorporating soft reward-based labels and hard ranking-based labels.
Instruction-based fine-tuning is employed to harmonise the training impact across different data sources using task-specific prompts.
Homogeneous in-batch negative sampling is used to ensure that the in-batch negatives contribute effectively to the discriminative power of embeddings for each specific task.
The authors discuss typical scenarios empowered by LLM-Embedder, focusing on knowledge enhancement, long-context modeling, in-context learning, and tool learning.
In each scenario, they describe what to store in the vector DB, what is used to query the vector DB, and how to leverage the retrieved data for augmenting LLMs.
The main objective of this work is to introduce LLM-Embedder, a unified embedding model that can satisfy the primary retrieval augmentation needs of LLMs.
By systematically optimizing the training methodology and demonstrating the effectiveness of LLM-Embedder in various retrieval augmentation scenarios, the authors aim to enhance the performance of LLMs across critical aspects such as knowledge enhancement, long-context modeling, and instruction following.
The authors conduct extensive experiments to evaluate the effectiveness of LLM-Embedder in various retrieval augmentation scenarios.
They compare LLM-Embedder with both general embedding models and task-specific embedding models.
The results show that LLM-Embedder outperforms the baselines in knowledge enhancement, in-context learning, long-context modeling, tool learning, and conversational search.
The ablation studies further confirm the importance of the key factors in LLM-Embedder's training process, such as using soft rewards from LLMs, stabilized distillation, instruction-based fine-tuning, and homogeneous in-batch negative sampling.
Develop an AI-powered personal assistant that leverages LLM-Embedder to provide comprehensive support for various user queries and tasks.
The assistant can retrieve relevant knowledge from a large knowledge base to answer user questions, access historical context to maintain long-term memory, retrieve appropriate examples to improve instruction following, and identify suitable tools to interact with the physical world.
By integrating LLM-Embedder, the personal assistant can deliver more accurate, context-aware, and actionable responses, enhancing user experience and productivity.
Create a domain-specific chatbot, such as a customer support chatbot for a specific industry (e.g., healthcare, finance, or e-commerce), using LLM-Embedder as the retrieval backbone.
The chatbot can be trained on domain-specific knowledge bases, conversation logs, and tool descriptions to provide accurate and relevant information to users.
LLM-Embedder's ability to retrieve knowledge, maintain context, and identify appropriate tools enables the chatbot to handle complex user queries, provide personalized recommendations, and guide users through various tasks within the specific domain.
Develop an AI-powered tutoring system that utilizes LLM-Embedder to provide personalized learning experiences for students.
The tutoring system can retrieve relevant educational content, such as explanations, examples, and exercises, based on the student's learning progress, interests, and questions.
LLM-Embedder's capability to retrieve examples and maintain long-term context allows the tutoring system to adapt to the student's learning pace, provide targeted feedback, and recommend suitable learning materials.
The system can also leverage tool retrieval to suggest interactive simulations, visualizations, or educational games to enhance the learning experience.
These practical applications demonstrate how LLM-Embedder can be utilized to create powerful and versatile AI systems that assist users in various domains. By leveraging LLM-Embedder's comprehensive retrieval augmentation capabilities, these systems can provide more accurate, context-aware, and actionable support, ultimately improving user experience and efficiency.
