Hybrid Context Retrieval Augmented Generation Pipeline: LLM-Augmented Knowledge Graphs and Vector Database for Accreditation Reporting Assistance
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenges associated with Large Language Models (LLMs) in the context of knowledge graph generation by proposing a Hybrid Context Retrieval Augmented Generation Pipeline. This pipeline combines retrieval and generation processes to enhance the accuracy and relevance of responses generated by LLMs . The challenges include issues such as retrieval limitations, missing crucial information, non-relevant contexts, and generation problems like hallucinations and responses not based on the provided context . While the use of LLMs for natural language tasks like text generation, summarization, and question-answering has been beneficial, the uncertainties in information extraction and limitations like hallucinations and knowledge constraints remain significant . The paper's focus on improving the retrieval process and enhancing the relevance of retrieved contexts to the original query represents a novel approach to mitigating the limitations of LLMs in knowledge graph generation .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the hypothesis that implementing a hybrid-context retrieval augmented generation pipeline can facilitate the report development process by allowing institutions to integrate their own documents into a database prepopulated with AACSB Standard data. Users can then query their institutional data alongside AACSB data to evaluate alignment with the standards . The goal is to enhance the ease and efficiency of assessing alignment with accreditation standards through the utilization of this pipeline .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper proposes a Hybrid Context Retrieval Augmented Generation Pipeline that integrates various innovative ideas, methods, and models for accreditation reporting assistance . One key concept introduced is the use of Large Language Models (LLMs) in conjunction with a knowledge graph and vector database to facilitate natural language responses to user queries . The pipeline allows institutions to incorporate their own documents into a prepopulated database containing AACSB Standard data, enabling users to evaluate alignment with standards . Additionally, the paper introduces the concept of Modular RAG, which extends beyond traditional RAG approaches by providing additional functionality through modules like "Search" and "Predict" . The pipeline's design aligns more closely with the Advanced RAG approach, focusing on query and index optimization for efficient document retrieval . Furthermore, the paper discusses the importance of chunking strategies in the indexing phase to enhance the retrieval process by splitting documents into manageable segments . The models used in the project include OpenAI's gpt-3.5 for tasks such as document summary classification, knowledge graph building, and context-based response generation, while GPT-4 is utilized for Cypher query generation from natural language . Additionally, the project leverages models like 'text-embedding-ada-002' for generating vector embeddings and 'pszemraj/led-large-book-summary' for document summarization . The Hybrid Context Retrieval Augmented Generation Pipeline introduces several key characteristics and advantages compared to previous methods .
- Modular RAG: This pipeline extends beyond Naive and Advanced RAG models by incorporating additional functionality through modules like "Search" and "Predict," offering flexibility in architecture patterns to enhance system performance .
- Advanced RAG Approach: Aligned with the Advanced RAG method, the pipeline focuses on query and index optimization, enabling efficient retrieval of relevant documents based on input queries .
- Index Optimization: The pipeline employs chunking strategies during the indexing phase to split documents into manageable segments, enhancing the retrieval process by maintaining context within the document .
- Query Optimization: Query optimization techniques are utilized to improve the effectiveness and quality of input queries, including query expansion, transformation, and routing .
- Multi-Source Retrieval: By integrating vector index data and knowledge graph data, the pipeline provides a dually grounded context for the generation task, ensuring accurate and relevant responses .
- Knowledge Graph Construction: The pipeline utilizes both manual construction and an 'LLM Augmented Knowledge Graph' approach to develop knowledge graphs, enhancing the context retrieval process .
- Future Work: The project suggests future enhancements such as implementing multi-label classification and experimenting with pruning the constructed knowledge graph to refine and improve the pipeline further .
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research papers exist in the field of Large Language Models (LLMs) and knowledge graphs:
- E. A. Vogels conducted a study on the awareness of CHATGPT among Americans .
- T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa explored the capabilities of large language models as zero-shot reasoners .
- W. X. Zhao et al. provided a survey of large language models .
- H. Naveed et al. offered a comprehensive overview of large language models .
- Z. Xu, S. Jain, and M. Kankanhalli discussed the inevitability of hallucinations in large language models .
- P. Lewis et al. delved into retrieval-augmented generation for knowledge-intensive NLP tasks .
- Y. Gao et al. conducted a survey on retrieval-augmented generation for large language models .
Noteworthy researchers in this field include:
- E. A. Vogels
- T. Kojima
- S. S. Gu
- M. Reid
- Y. Matsuo
- Y. Iwasawa
- W. X. Zhao
- H. Naveed
- Z. Xu
- S. Jain
- M. Kankanhalli
- P. Lewis
- Y. Gao
The key to the solution mentioned in the paper involves the concept of Retrieval Augmented Generation (RAG), which is a system architecture approach enabling Large Language Models (LLMs) to utilize external contextual information for generating responses to input prompts. This architecture overcomes limitations of standalone LLMs by leveraging external context for response generation .
How were the experiments in the paper designed?
The experiments in the paper were designed to evaluate the Hybrid Context Retrieval Augmented Generation Pipeline. The pipeline aims to assist in the accreditation reporting process by integrating institutional documents with AACSB Standard data, allowing users to query their own data alongside AACSB data to assess alignment with the standards . The pipeline utilizes a sequence of steps, including user query initiation, query optimization, query embedding, query conversion, vector index context retrieval, knowledge graph context retrieval, generation of responses, and returning the generated response to the user . The experiments focused on assessing the pipeline's performance metrics such as context relevance, faithfulness, answer relevancy, context recall, and answer correctness . The design involved testing various queries related to AACSB standards and institutional contexts to evaluate the pipeline's effectiveness in providing accurate and relevant responses to user queries .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the project is the RAGAs framework . The code for the project is open source and available on GitHub at the following link: https://github.com/CS-Edwards/advRAG .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of the Hybrid Context Retrieval Augmented Generation Pipeline, focusing on metrics such as context relevance, faithfulness, answer relevancy, context recall, and answer correctness . The results indicate that the pipeline achieved a high level of answer correctness, with a median score of 1.000, demonstrating the effectiveness of the approach in generating accurate responses . Additionally, the pipeline's context recall metric was notably high, further emphasizing the system's ability to retrieve relevant information from the knowledge base .
Furthermore, the paper outlines the implementation of a hybrid-context retrieval augmented generation pipeline, which integrates institutional data with AACSB Standard data to facilitate the evaluation of alignment with accreditation standards . This approach allows users to query their institutional data alongside AACSB data, enabling them to assess how well their documents align with the standards . By leveraging this pipeline, institutions can streamline the report development process and enhance the alignment of their documents with accreditation standards .
Overall, the experiments and results detailed in the paper demonstrate a robust methodology and provide strong empirical support for the scientific hypotheses under investigation. The use of metrics to evaluate the pipeline's performance, along with the practical implementation of the system for accreditation reporting assistance, underscores the effectiveness and reliability of the proposed approach .
What are the contributions of this paper?
The paper discusses the implementation of a hybrid context retrieval augmented generation pipeline that integrates large language models into various stages of development . The observations from the project highlight that the pipeline performed better on AACSB-related queries compared to institution-related queries, achieving optimal scores in 'answer relevancy' and near-optimal scores in 'context recall' . The structured nature of the knowledge graph built manually contributed to the improved performance on AACSB queries, allowing for more accurate references and effective Cypher queries . Additionally, the paper mentions testing different models for classifying document summaries into 'Standard' categories, with observations indicating that the GPT-4 model struggled in this task, often classifying documents as '0', representing a lack of alignment with a specific standard .
What work can be continued in depth?
To delve deeper into the work related to accreditation reporting assistance, a focus can be placed on the continuous improvement processes outlined in the AACSB standards. This involves ongoing efforts such as:
- Ensuring the quality of teaching through systematic assessment processes .
- Enhancing faculty teaching through development activities to align with program goals .
- Demonstrating positive societal impact through internal and external initiatives .
- Maintaining well-documented assurance of learning processes for program improvement .
- Implementing policies for learner progression and post-graduation success .
- Producing impactful intellectual contributions and engaging with external stakeholders for knowledge transfer .
- Promoting innovation, experiential learning, and lifelong learning in the curriculum .
- Supporting faculty and professional staff development over their careers .
- Managing curriculum content to ensure relevance, alignment with program goals, and agility with emerging technologies .
- Fostering learner engagement and academic/professional interactions .