Improving Retrieval for RAG based Question Answering Models on Financial Documents
(2404.07221)Abstract
The effectiveness of LLMs in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It explore strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries.
Overview
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The paper addresses challenges in Retrieval Augmented Generation (RAG) pipelines used with LLMs for question answering (QA) on financial documents, focusing on enhancing retrieval quality to improve QA accuracy.
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Proposed methods include advanced chunking techniques like recursive and element-based chunking, query expansion with Hypothetical Document Embeddings (HyDE), enriched metadata annotations, re-ranking algorithms, and fine-tuning embedding algorithms for domain specificity.
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Evaluations were conducted using the FinanceBench dataset, with metrics like retrieval quality, answer accuracy, and answer faithfulness to measure the efficacy of the proposed enhancements.
Enhancing Retrieval for RAG-Based QA Systems in Financial Document Analysis
The paper "Improving Retrieval for RAG based Question Answering Models on Financial Documents" by Spurthi Setty, Katherine Jijo, Eden Chung, and Natan Vidra addresses the core challenges in the Retrieval Augmented Generation (RAG) pipelines used in conjunction with LLMs for question answering (QA) tasks, particularly within the context of financial documents.
Key Insights and Issues in RAG Pipelines
The foundational premise is the dependence of LLMs on the quality of retrieved text chunks, which significantly influences the output accuracy in QA tasks. Although LLMs have achieved substantial advancements, their performance is often hindered by the retrieval component of RAG, especially when dealing with domain-specific information. Financial information retrieval faces acute challenges due to the complexity and density of financial reports.
RAG pipelines traditionally adopt a simplistic chunking approach, typically dividing documents into uniform chunks which then undergo a similarity search (often based on cosine similarity) to match a user's query with relevant document chunks. However, this approach is fraught with limitations:
Chunking Strategies:
- Uniform chunking methods disregard document structure, leading to potential loss and fragmentation of context.
- Important information might span across multiple sections, making it difficult for fixed-size chunks to encapsulate all necessary data for accurate QA.
Semantic Search Limitations:
- Similarity does not always equate to relevance, which can result in retrieving irrelevant or contradictory information.
- Standard embeddings might lack domain-specific nuances, adversely affecting retrieval accuracy.
Complex Document Structures:
- Financial documents often contain complex structures, such as tables and segmented headings, posing additional challenges.
Proposed Enhancements
The paper presents several methodological innovations to enhance the retrieval aspect of RAG pipelines for financial documents:
Advanced Chunking Techniques:
- Recursive Chunking: Utilizes NLP tools to create more context-aware chunks by considering punctuation and sentence boundaries.
- Element-Based Chunking: Specifically tailored for financial reports, starting new chunks at title or table elements to preserve the integrity and context crucial for accurate QA.
Query Expansion Techniques:
- Hypothetical Document Embeddings (HyDE): Enhances retrieval by generating hypothetical answers to queries and then conducting similarity searches with both the original query and the generated answer. This method helps in approximating the logical steps a human analyst would take.
Metadata Annotations and Indexing:
- Enhanced indexing methods enrich chunks with metadata annotations, such as document type, table identifiers, and keywords, facilitating more precise retrieval when dealing with multiple documents.
Re-ranking Algorithms:
- Post-retrieval re-ranking algorithms (e.g., Cohere's ReRank) prioritize relevance over mere similarity, ensuring the most pertinent chunks are selected for context augmentation.
Fine-Tuning Embedding Algorithms:
- Embedding algorithms can be fine-tuned with domain-specific datasets to improve retrieval relevance and performance, taking advantage of dynamic embeddings like those from OpenAI to interpret domain-specific nuances more accurately.
Evaluation Metrics
The paper emphasizes structured and unstructured methods to evaluate RAG system efficacy:
- Retrieval Quality: Assessed using page-level and paragraph-level accuracy, alongside context relevance scores from frameworks like RAGAS.
- Answer Accuracy: Evaluated through BLEU and Rouge-L scores, as well as answer faithfulness metrics to measure the grounding of generated answers in the retrieved context, mitigating hallucination risks.
Dataset and Experimental Context
The empirical analysis leverages the FinanceBench dataset by Patronus AI, encompassing over 10,231 questions related to various financial documents (e.g., 10-K, 10-Q reports) of publicly traded US companies. This benchmark dataset enables comprehensive evaluation of retrieval and generation capabilities within financial QA tasks.
Implications and Future Directions
The research underscores the critical role of robust retrieval mechanisms in enhancing overall system performance for domain-specific QA tasks. The proposed techniques address significant bottlenecks in existing RAG pipelines and offer a framework that can be adapted across diverse industries such as healthcare and legal domains.
Future research could explore the integration of knowledge graphs to help retrieval systems handle multi-step logical queries and further fine-tune domain-specific embeddings. These advancements can aid in navigating the intrinsic complexity of financial documents and other knowledge-intensive domains.
Conclusion
The paper provides a structured approach to resolving key limitations in RAG pipelines by implementing context-aware chunking, query expansion, and metadata indexing, along with re-ranking and fine-tuning of embeddings. These enhancements are pivotal in improving the performance of LLMs in QA tasks, especially in finance, and set a foundation for further advancements in domain-specific information retrieval.
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