Multiple Abstraction Level Retrieve Augment Generation

• URL: http://arxiv.org/abs/2501.16952v1
• Date: Tue, 28 Jan 2025 13:49:39 GMT
• Title: Multiple Abstraction Level Retrieve Augment Generation
• Authors: Zheng Zheng, Xinyi Ni, Pengyu Hong,
• Abstract summary: A Retrieval-Augmented Generation (RAG) model powered by a large language model (LLM) provides a faster and more cost-effective solution for adapting to new data and knowledge.<n>We propose a novel RAG approach that uses chunks of multiple abstraction levels (MAL), including multi-sentence-level, paragraph-level, section-level, and document-level.<n>Compared to traditional single-level RAG approaches, our approach improves AI evaluated answer correctness of Q/A by 25.739% on Glyco-related papers.
• Score: 4.516242893120263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A Retrieval-Augmented Generation (RAG) model powered by a large language model (LLM) provides a faster and more cost-effective solution for adapting to new data and knowledge. It also delivers more specialized responses compared to pre-trained LLMs. However, most existing approaches rely on retrieving prefix-sized chunks as references to support question-answering (Q/A). This approach is often deployed to address information needs at a single level of abstraction, as it struggles to generate answers across multiple levels of abstraction. In an RAG setting, while LLMs can summarize and answer questions effectively when provided with sufficient details, retrieving excessive information often leads to the 'lost in the middle' problem and exceeds token limitations. We propose a novel RAG approach that uses chunks of multiple abstraction levels (MAL), including multi-sentence-level, paragraph-level, section-level, and document-level. The effectiveness of our approach is demonstrated in an under-explored scientific domain of Glycoscience. Compared to traditional single-level RAG approaches, our approach improves AI evaluated answer correctness of Q/A by 25.739\% on Glyco-related papers.

Related papers

• ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering [54.72902502486611]
  ReAG is a Reasoning-Augmented Multimodal RAG approach that combines coarse- and fine-grained retrieval with a critic model that filters irrelevant passages.<n>ReAG significantly outperforms prior methods, improving answer accuracy and providing interpretable reasoning grounded in retrieved evidence.
  arXiv  Detail & Related papers  (2025-11-27T19:01:02Z)
• URaG: Unified Retrieval and Generation in Multimodal LLMs for Efficient Long Document Understanding [55.45331924836242]
  We present URaG, a framework that Unifies Retrieval and Generation within a single MLLM.<n>We show that URaG achieves state-of-the-art performance while reducing computational overhead by 44-56%.
  arXiv  Detail & Related papers  (2025-11-13T17:54:09Z)
• Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training [50.37345200692884]
  We propose Q-RAG, a novel approach that fine-tunes Embedder model for multi-step retrieval using reinforcement learning (RL)<n>Q-RAG offers a competitive, resource-efficient alternative to existing multi-step retrieval methods for open-domain question answering.
  arXiv  Detail & Related papers  (2025-11-10T17:31:02Z)
• MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval [50.30107119622642]
  Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data.<n>Retrieval-Augmented Generation (RAG) addresses this issue by grounding LLMs in external knowledge.<n>MarAG-R1 is a reinforcement-learned multi-tool RAG framework that enables LLMs to dynamically coordinate multiple retrieval mechanisms.
  arXiv  Detail & Related papers  (2025-10-31T15:51:39Z)
• Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts [6.14987671066697]
  In this paper, we propose Tagging-Augmented Generation (TAG) as a lightweight data augmentation strategy for long-context scenarios.<n>We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks -- NoLima and NovelQA.<n>We show that tagging the context or even just adding tag definitions into QA prompts leads to consistent performance gains over the baseline.
  arXiv  Detail & Related papers  (2025-10-27T03:23:25Z)
• DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router [57.28685457991806]
  DeepSieve is an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router.<n>Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design.
  arXiv  Detail & Related papers  (2025-07-29T17:55:23Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering [15.04887070246276]
  Large language models have recently pushed open domain question answering to new frontiers. prevailing retriever-reader pipelines often depend on multiple rounds of prompt level instructions. We propose EmbQA, an embedding-level framework that enhances both the retriever and the reader.
  arXiv  Detail & Related papers  (2025-03-03T14:41:35Z)
• SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph [10.297615455470133]
  Multi-entity question answering (MEQA) poses significant challenges for large language models. This paper introduces a structured RAG framework that organizes extracted entities into relational tables. Experiments on Wikipedia-based multi-entity QA tasks demonstrate that SRAG significantly outperforms state-of-the-art long-context LLMs.
  arXiv  Detail & Related papers  (2025-03-03T09:37:33Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings. Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA [78.45521005703958]
  multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge. We propose a novel framework called textbfRetrieval-textbfReftextbfAugmented textbfGeneration (mR$2$AG) which achieves adaptive retrieval and useful information localization. mR$2$AG significantly outperforms state-of-the-art MLLMs on INFOSEEK and Encyclopedic-VQA
  arXiv  Detail & Related papers  (2024-11-22T16:15:50Z)
• How Much Can RAG Help the Reasoning of LLM? [9.601957219734683]
  Retrieval-Augmented Generation (RAG) has gained significant popularity in modern Large Language Models (LLMs) How does RAG help the reasoning process and can RAG help improve the reasoning capability remains question.
  arXiv  Detail & Related papers  (2024-10-03T09:48:09Z)
• W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering [28.79851078451609]
  We propose W-RAG, a method that draws weak training signals from the downstream task and fine-tunes the retriever to prioritize passages that most benefit the task. We conduct comprehensive experiments across four publicly available OpenQA datasets to demonstrate that our approach enhances both retrieval and OpenQA performance.
  arXiv  Detail & Related papers  (2024-08-15T22:34:44Z)
• RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering [61.19126689470398]
  Long-form RobustQA (LFRQA) is a new dataset covering 26K queries and large corpora across seven different domains. We show via experiments that RAG-QA Arena and human judgments on answer quality are highly correlated. Only 41.3% of the most competitive LLM's answers are preferred to LFRQA's answers, demonstrating RAG-QA Arena as a challenging evaluation platform for future research.
  arXiv  Detail & Related papers  (2024-07-19T03:02:51Z)
• Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation [51.8188846284153]
  RAG has been widely adopted to enhance Large Language Models (LLMs) Attributed Text Generation (ATG) has attracted growing attention, which provides citations to support the model's responses in RAG. This paper proposes a fine-grained ATG method called ReClaim(Refer & Claim), which alternates the generation of references and answers step by step.
  arXiv  Detail & Related papers  (2024-07-01T20:47:47Z)
• DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph [70.79413606968814]
  We introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks.
  arXiv  Detail & Related papers  (2024-06-25T04:27:53Z)
• Multi-Head RAG: Solving Multi-Aspect Problems with LLMs [18.581518952488093]
  Multi-Head RAG (MRAG) is a novel scheme for fetching multi-aspect documents.<n>We show MRAG's design advantages over 18 RAG baselines, empirical improvements of up to 20% in retrieval success ratios.
  arXiv  Detail & Related papers  (2024-06-07T16:59:38Z)
• Improving Retrieval for RAG based Question Answering Models on Financial Documents [0.046603287532620746]
  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.
  arXiv  Detail & Related papers  (2024-03-23T00:49:40Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Enhancing textual textbook question answering with large language models and retrieval augmented generation [3.6799953119508735]
  We propose a framework (PLRTQA) that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario.<n>Our architecture outperforms the baseline, achieving an accuracy improvement of 4. 12% in the validation set and 9. 84% in the test set for textual multiple-choice questions.
  arXiv  Detail & Related papers  (2024-02-05T11:58:56Z)

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.