arXiv 2409.136992024-09-05cs.IR

问答系统中的越南语法律信息检索

Vietnamese Legal Information Retrieval in Question-Answering System

Thiem Nguyen Ba, Vinh Doan The, Tung Pham Quang, Toan Tran Van

备注：: 7 pages

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AI总结：

针对越南语法律问答系统中数据处理低效和检索结果排序不佳的问题，提出通过优化数据处理、归一化改进Reciprocal Rank Fusion并结合Active Retrieval重排序的方法，显著提升了综合QA系统的性能与可靠性。

摘要：

在数据量快速增长的现代，准确检索和推荐相关文档对于提升问答（QA）系统的可靠性变得至关重要。近期，检索增强生成（RAG）因能缓解QA系统中的幻觉问题从而增强大语言模型（LLMs）的能力而受到广泛认可，这在法律领域尤为有益。已有研究提出了多种方法，例如使用稠密向量嵌入的语义搜索，或将多种技术结合以在输入LLMs前改善结果。然而，由于若干挑战，这些方法在应用于越南语时往往表现不佳：即低效的越南语数据处理导致token长度过长，或过于简单的集成技术导致不稳定且改进有限。此外，一个常被忽视的关键问题是最终相关文档的排序，这些文档被用作参考以确保LLMs提供答案的准确性。在本报告中，我们介绍了为应对这些挑战所采取的三项主要修改。首先，我们探索了多种实用的数据处理方法，以克服嵌入模型的局限性。其次，我们通过归一化排序来增强Reciprocal Rank Fusion，从而有效结合关键词搜索和向量搜索的结果。我们还使用Active Retrieval对LLMs使用的源信息片段进行细致的重排序，以改善精炼生成信息时的用户体验。我们认为，该技术也可被视为一种新的重排序方法，可能替代传统的cross encoder。最后，我们将这些技术整合到一个完整的QA系统中，显著提升了其性能和可靠性。

英文摘要：

In the modern era of rapidly increasing data volumes, accurately retrieving and recommending relevant documents has become crucial in enhancing the reliability of Question Answering (QA) systems. Recently, Retrieval Augmented Generation (RAG) has gained significant recognition for enhancing the capabilities of large language models (LLMs) by mitigating hallucination issues in QA systems, which is particularly beneficial in the legal domain. Various methods, such as semantic search using dense vector embeddings or a combination of multiple techniques to improve results before feeding them to LLMs, have been proposed. However, these methods often fall short when applied to the Vietnamese language due to several challenges, namely inefficient Vietnamese data processing leading to excessive token length or overly simplistic ensemble techniques that lead to instability and limited improvement. Moreover, a critical issue often overlooked is the ordering of final relevant documents which are used as reference to ensure the accuracy of the answers provided by LLMs. In this report, we introduce our three main modifications taken to address these challenges. First, we explore various practical approaches to data processing to overcome the limitations of the embedding model. Additionally, we enhance Reciprocal Rank Fusion by normalizing order to combine results from keyword and vector searches effectively. We also meticulously re-rank the source pieces of information used by LLMs with Active Retrieval to improve user experience when refining the information generated. In our opinion, this technique can also be considered as a new re-ranking method that might be used in place of the traditional cross encoder. Finally, we integrate these techniques into a comprehensive QA system, significantly improving its performance and reliability

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