Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata

• URL: http://arxiv.org/abs/2406.13213v2
• Date: Mon, 19 Aug 2024 11:38:14 GMT
• Title: Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata
• Authors: Mykhailo Poliakov, Nadiya Shvai,
• Abstract summary: Retrieval-augmented generation (RAG) enables retrieval of relevant information from an external knowledge source. Traditional RAG applications perform poorly in answering multi-hop questions. We introduce a new method called Multi-Meta-RAG, which uses database filtering with LLM-extracted metadata.
• Score: 1.6574413179773757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The retrieval-augmented generation (RAG) enables retrieval of relevant information from an external knowledge source and allows large language models (LLMs) to answer queries over previously unseen document collections. However, it was demonstrated that traditional RAG applications perform poorly in answering multi-hop questions, which require retrieving and reasoning over multiple elements of supporting evidence. We introduce a new method called Multi-Meta-RAG, which uses database filtering with LLM-extracted metadata to improve the RAG selection of the relevant documents from various sources, relevant to the question. While database filtering is specific to a set of questions from a particular domain and format, we found out that Multi-Meta-RAG greatly improves the results on the MultiHop-RAG benchmark. The code is available at https://github.com/mxpoliakov/Multi-Meta-RAG.

Related papers

• M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding [63.33447665725129]
  We introduce M3DocRAG, a novel multi-modal RAG framework that flexibly accommodates various document contexts. M3DocRAG can efficiently handle single or many documents while preserving visual information. We also present M3DocVQA, a new benchmark for evaluating open-domain DocVQA over 3,000+ PDF documents with 40,000+ pages.
  arXiv  Detail & Related papers  (2024-11-07T18:29:38Z)
• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [102.31558123570437]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs) We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs [78.5013630951288]
  This paper introduces techniques for advancing information retrieval with multimodal large language models (MLLMs) We first study fine-tuning an MLLM as a bi-encoder retriever on 10 datasets with 16 retrieval tasks. We propose modality-aware hard negative mining to mitigate the modality bias exhibited by MLLM retrievers.
  arXiv  Detail & Related papers  (2024-11-04T20:06:34Z)
• EfficientRAG: Efficient Retriever for Multi-Hop Question Answering [52.64500643247252]
  We introduce EfficientRAG, an efficient retriever for multi-hop question answering. Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.
  arXiv  Detail & Related papers  (2024-08-08T06:57:49Z)
• Database-Augmented Query Representation for Information Retrieval [59.57065228857247]
  We present a novel retrieval framework called Database-Augmented Query representation (DAQu) DAQu augments the original query with various (query-related) metadata across multiple tables. We validate DAQu in diverse retrieval scenarios that can incorporate metadata from the relational database.
  arXiv  Detail & Related papers  (2024-06-23T05:02:21Z)
• DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering [4.364937306005719]
  RAG has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA) We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. A two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers.
  arXiv  Detail & Related papers  (2024-06-11T15:15:33Z)
• Multi-Head RAG: Solving Multi-Aspect Problems with LLMs [13.638439488923671]
  Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) Existing RAG solutions do not focus on queries that may require fetching multiple documents with substantially different contents. This paper introduces Multi-Head RAG (MRAG), a novel scheme designed to address this gap with a simple yet powerful idea.
  arXiv  Detail & Related papers  (2024-06-07T16:59:38Z)
• MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries [22.4349439498591]
  Retrieval-augmented generation (RAG) augments large language models (LLM) by retrieving relevant knowledge. Existing RAG systems are inadequate in answering multi-hop queries, which require retrieving and reasoning over multiple pieces of supporting evidence. We develop a novel dataset, MultiHop-RAG, which consists of a knowledge base, a large collection of multi-hop queries, their ground-truth answers, and the associated supporting evidence.
  arXiv  Detail & Related papers  (2024-01-27T11:41:48Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.