Long-Context RAG Dataset Generation for LLM Training Evaluation
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Solution Overview
Problem
Training and evaluating retrieval augmented generation (RAG) large language models (LLMs) with long contexts is challenging due to the scarcity of available training and testing data.
Innovation Solution
A data pipeline is developed to generate a dataset of long-context documents for training and evaluating RAG LLMs, where an LLM generates subtopics and insights, which are combined to form documents, allowing the RAG LLM to retrieve relevant documents and generate answers, with the dataset serving as a ground-truth for training and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RAG LLMs are trained with long-context documents, then the ability to generate accurate answers based on long contexts is improved, but the scarcity of training and testing data limits effective training and evaluation
Solution Approach 1:
The system performs preliminary actions by using an LLM to generate subtopics and insights before forming the final long-context documents. This preparatory step creates structured content that can be systematically combined into training documents, addressing the data scarcity issue by proactively generating training materials rather than relying on pre-existing datasets.
Solution Approach 2:
The system introduces an intermediary process where an LLM generates subtopics and insights that serve as building blocks for long-context documents. These intermediaries (subtopics and insights) bridge the gap between available data and the required long-context training data, enabling systematic construction of training documents without requiring large amounts of pre-existing long-context data.
2Quantity of substance
If a data pipeline is developed to generate long-context documents using LLM-generated subtopics and insights, then training data availability is improved, but the system complexity increases
Solution Approach 1:
The system segments the document generation process into distinct components: generating subtopics, generating insights for each subtopic, and combining them into final documents. This segmentation allows each component to be handled independently and systematically, managing complexity by breaking down the overall task into smaller, more manageable steps that can be automated through the data pipeline.
Solution Approach 2:
The system employs a universal LLM that performs multiple functions within the data pipeline: generating subtopics, generating insights, and potentially forming documents. This multi-functional approach reduces overall system complexity by using a single versatile model rather than requiring separate specialized components for each generation task.
Data Source
AI summary
Embodiments described herein provide a method for configuring an artificial intelligence (AI) conversation bot to respond to a user query based on retrieved contextual documents. The method includes: receiving, via a communication interface, a user query comprising a natural language description of a topic; generating, by a first neural network based language model, one or more subtopics of the topic based on a first input prompt combining the topic and a first instruction to generate the one or more subtopics; generating, by the first neural network based language model, one or more statements for at least one of the subtopics based on a second input prompt combining the one or more subtopics and a second instruction to generate the one or more statements; and generating, by the first neural network based language model, at least one document containing a set of randomly selected statements from the one or more statements.