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Improving Retrieval for RAG based Question Answering Models on Financial Documents

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arxiv 2404.07221 v2 pith:LZGLMVOJ submitted 2024-03-23 cs.IR cs.CLcs.LGq-fin.GN

Spurthi Setty , Harsh Thakkar , Alyssa Lee , Eden Chung , Natan Vidra This is my paper · ORCID
classification cs.IRcs.CLcs.LGq-fin.GN
keywords llmsretrievalqualitytextalgorithmschunkmodelsqueries
verification ladder T0 review T1 audit T2 compute T3 formal
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The effectiveness of Large Language Models (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 delves into 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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generation

    cs.CR 2025-08 conditional novelty 6.0 of 10

    A universal knowledge-corruption attack uses as few as 100 crafted texts to hijack responses to thousands of diverse user queries in retrieval-augmented generation.

  2. KinyaColBERT: A Lexically Grounded Retrieval Model for Low-Resource Retrieval-Augmented Generation

    cs.CL 2025-07 conditional novelty 5.0 of 10

    KinyaColBERT, a morphology-aware two-tier ColBERT retriever, reports large MRR gains over multilingual baselines and commercial APIs on a new Kinyarwanda agricultural retrieval benchmark.

  3. Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy

    cs.IR 2025-02 conditional novelty 5.0 of 10

    A three-stage RAG pipeline generates a cited summary (GenText) from PubMed Central passages and ranks health documents by topical relevance plus alignment with that summary, outperforming baselines on CLEF eHealth and...

  4. Hierarchical Reranking for Scalable Financial RAG System

    cs.IR 2026-07 reject novelty 4.0 of 10

    A finance-specific RAG pipeline combining table-to-JSON conversion, two-stage reranking, and long-context split-fusion reports NDCG@20=0.7918 and second place in the ICAIF '24 FinanceRAG challenge.

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