Enhancing Customer Service Question Answering with Retrieval-Augmented Generation and Knowledge Graphs

Grunnleggende konsepter

Integrating retrieval-augmented generation (RAG) with a knowledge graph (KG) improves the accuracy of retrieving relevant past issues and the quality of generated answers for customer service inquiries.

Sammendrag

The authors introduce a novel customer service question-answering method that combines retrieval-augmented generation (RAG) with a knowledge graph (KG). The key insights are:

Intra-issue Structure Preservation: The method constructs a KG from historical customer service issue tickets, retaining the inherent structure and relationships between different sections of each ticket. This preserves crucial information that is lost when treating tickets as plain text.

Inter-issue Relation Modeling: The KG also captures the explicit and implicit connections between different issue tickets, enabling more accurate retrieval of relevant past issues.

Coherent Answer Generation: By leveraging the structured KG, the method can generate more complete and coherent answers by avoiding the issue of text segmentation, which can lead to the disconnection of related content.

The empirical evaluation shows that the proposed method outperforms the baseline by 77.6% in Mean Reciprocal Rank (MRR) for retrieval and 0.32 in BLEU score for answer generation. The method has also been deployed in LinkedIn's customer service team, reducing the median per-issue resolution time by 28.6%.

Statistikk

The proposed method outperforms the baseline by 77.6% in Mean Reciprocal Rank (MRR) for retrieval.
The proposed method outperforms the baseline by 0.32 in BLEU score for answer generation.
The deployment of the proposed method in LinkedIn's customer service team reduced the median per-issue resolution time by 28.6%.

Sitater

"The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance."
"Our method constructs a KG from historical issues for use in retrieval, retaining the intra-issue structure and inter-issue relations."
"This integration of a KG not only improves retrieval accuracy by preserving customer service structure information but also enhances answering quality by mitigating the effects of text segmentation."

Viktige innsikter hentet fra

Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

by Zhentao Xu,M... klokken arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17723.pdf

Dypere Spørsmål

How can the automated extraction of graph templates be improved to enhance the system's adaptability to different domains?

Automated extraction of graph templates can be enhanced by incorporating more advanced natural language processing (NLP) techniques. One approach could involve utilizing pre-trained language models like BERT or GPT to understand the structure and content of the text within the knowledge graph. These models can help in identifying key entities, relationships, and attributes within the text data, which can then be used to automatically generate graph templates. Additionally, incorporating entity recognition and relation extraction algorithms can further improve the accuracy of template extraction.
Furthermore, implementing a feedback loop mechanism where users can provide input on the extracted templates can help refine and adapt the system to different domains. By incorporating user feedback, the system can continuously learn and improve its template extraction process, making it more adaptable to varying contexts and domains.

How can the applicability of this approach be extended beyond customer service to other knowledge-intensive domains, such as technical support or medical diagnosis?

To extend the applicability of this approach to other knowledge-intensive domains, such as technical support or medical diagnosis, several strategies can be explored:

Domain-specific Knowledge Graphs: Develop domain-specific knowledge graphs tailored to the unique characteristics and requirements of each domain. For technical support, the knowledge graph can include information on software systems, error codes, and troubleshooting steps. In the medical domain, the graph can contain information on diseases, symptoms, treatments, and drug interactions.

Customized Query Intent Detection: Adapt the system to recognize and interpret query intents specific to each domain. For technical support, intents could include troubleshooting, configuration, or installation queries. In medical diagnosis, intents could involve symptom analysis, disease identification, or treatment recommendations.

Specialized Answer Generation: Customize the answer generation process to provide domain-specific responses. In technical support, the system can generate solutions to software issues or provide step-by-step troubleshooting guides. In medical diagnosis, the system can offer recommendations based on symptoms, medical history, and known conditions.

Continuous Learning and Improvement: Implement mechanisms for continuous learning from user interactions and feedback. This can help the system adapt to evolving trends, new information, and user preferences within different domains.

By incorporating these strategies and tailoring the system to the specific requirements of each knowledge-intensive domain, the approach can be successfully extended beyond customer service to address a wide range of complex problem-solving tasks.

How can dynamic updates to the knowledge graph based on user queries be enabled, improving real-time responsiveness?

Enabling dynamic updates to the knowledge graph based on user queries can significantly enhance real-time responsiveness. Here are some strategies to achieve this:

Real-time Data Ingestion: Implement a system that continuously monitors incoming user queries and updates the knowledge graph in real-time. This can involve integrating streaming data processing technologies to ingest, process, and update the graph with new information as it becomes available.

Contextual Understanding: Develop algorithms that can analyze the context of user queries and identify relevant updates to the knowledge graph. By understanding the intent and content of the queries, the system can determine which parts of the graph need to be updated or expanded.

Automated Graph Maintenance: Implement automated processes that can validate, clean, and maintain the integrity of the knowledge graph. This includes identifying outdated information, resolving inconsistencies, and ensuring that the graph remains accurate and up-to-date.

User Feedback Integration: Incorporate mechanisms for users to provide feedback on the relevance and accuracy of the information retrieved from the knowledge graph. User feedback can be used to validate updates and improve the overall quality of the graph.

By implementing these strategies, the system can dynamically update the knowledge graph based on user queries, ensuring real-time responsiveness and providing users with the most relevant and accurate information.