Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval

• URL: http://arxiv.org/abs/2411.00689v1
• Date: Fri, 01 Nov 2024 15:50:58 GMT
• Title: Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval
• Authors: Qingfei Zhao, Ruobing Wang, Xin Wang, Daren Zha, Nan Mu,
• Abstract summary: Existing Adaptive RAG (ARAG) systems struggle to effectively explore multiple retrieval sources due to their inability to select the right source at the right time. We propose a multi-source ARAG framework, termed MSPR, which synergizes reasoning and preference-driven retrieval to adaptive decide "when and what to retrieve" and "which retrieval source to use"
• Score: 4.862780562808097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a reliable external knowledge augmentation technique to mitigate hallucination issues and parameterized knowledge limitations in Large Language Models (LLMs). Existing Adaptive RAG (ARAG) systems struggle to effectively explore multiple retrieval sources due to their inability to select the right source at the right time. To address this, we propose a multi-source ARAG framework, termed MSPR, which synergizes reasoning and preference-driven retrieval to adaptive decide "when and what to retrieve" and "which retrieval source to use". To better adapt to retrieval sources of differing characteristics, we also employ retrieval action adjustment and answer feedback strategy. They enable our framework to fully explore the high-quality primary source while supplementing it with secondary sources at the right time. Extensive and multi-dimensional experiments conducted on three datasets demonstrate the superiority and effectiveness of MSPR.

Related papers

• Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization [97.72503890388866]
  We propose Self-Routing RAG (SR-RAG), a novel framework that binds selective retrieval with knowledge verbalization. SR-RAG enables an LLM to dynamically decide between external retrieval and verbalizing its own parametric knowledge. We introduce dynamic knowledge source inference via nearest neighbor search to improve the accuracy of knowledge source decision.
  arXiv  Detail & Related papers  (2025-04-01T17:59:30Z)
• Retrieval-Augmented Generation with Hierarchical Knowledge [38.500133410610495]
  Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. Existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition. We introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems.
  arXiv  Detail & Related papers  (2025-03-13T08:22:31Z)
• Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation [18.570899885235104]
  We propose Ext2Gen, a novel extract-then-generate model that enhances RAG by extracting query-relevant sentences before generating answers. Experiments demonstrate that Ext2Gen effectively identifies query-relevant sentences with high precision and recall, leading to highly reliable answers.
  arXiv  Detail & Related papers  (2025-02-28T06:46:53Z)
• Retrieval-Augmented Generation with Estimation of Source Reliability [15.69681944254975]
  Reliability-Aware RAG (RA-RAG) estimates the reliability of multiple sources and incorporates this information into both retrieval and aggregation processes. We introduce a benchmark designed to reflect real-world scenarios with heterogeneous source reliability.
  arXiv  Detail & Related papers  (2024-10-30T12:09:29Z)
• RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards [78.74923079748521]
  Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. Current approaches use instruction tuning to optimize LLMs, improving their ability to utilize retrieved knowledge. We propose a Differentiable Data Rewards ( DDR) method, which trains RAG systems by aligning data preferences between different RAG modules.
  arXiv  Detail & Related papers  (2024-10-17T12:53:29Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• Reward-RAG: Enhancing RAG with Reward Driven Supervision [43.66966457772646]
  We introduce Reward-RAG, a novel approach designed to enhance the Retrieval-Augmented Generation (RAG) model through Reward-Driven Supervision. Unlike previous RAG methodologies, our method adapts retrieval information to specific domains by employing CriticGPT to train a dedicated reward model. This reward model generates synthesized datasets for fine-tuning the RAG, aligning its outputs more closely with human preferences.
  arXiv  Detail & Related papers  (2024-10-03T15:26:50Z)
• RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation [54.707460684650584]
  Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG) RAGLAB is a modular and research-oriented open-source library that reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms.
  arXiv  Detail & Related papers  (2024-08-21T07:20:48Z)
• Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models [53.547190001324665]
  We propose REKI to acquire two types of external knowledge about users and items from large language models (LLMs) We develop individual knowledge extraction and collective knowledge extraction tailored for different scales of scenarios, effectively reducing offline resource consumption. Experiments demonstrate that REKI outperforms state-of-the-art baselines and is compatible with lots of recommendation algorithms and tasks.
  arXiv  Detail & Related papers  (2024-08-20T03:45:24Z)
• Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting [68.90949377014742]
  Speculative RAG is a framework that leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, distilled specialist LM. Our method accelerates RAG by delegating drafting to the smaller specialist LM, with the larger generalist LM performing a single verification pass over the drafts. It notably enhances accuracy by up to 12.97% while reducing latency by 50.83% compared to conventional RAG systems on PubHealth.
  arXiv  Detail & Related papers  (2024-07-11T06:50:19Z)
• Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation [64.7982176398485]
  Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of large language models (LLMs) We propose DPA-RAG, a universal framework designed to align diverse knowledge preferences within RAG systems.
  arXiv  Detail & Related papers  (2024-06-26T18:26:53Z)
• MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources [12.783393023641505]
  We introduce an efficient memory-augmented transformer called MATTER. MATTER retrieves and reads from both unstructured sources (paragraphs) and semi-structured sources (QA pairs) in the form of fixed-length neural memories. We demonstrate that our model outperforms existing efficient retrieval-augmented models on popular QA benchmarks in terms of both accuracy and speed.
  arXiv  Detail & Related papers  (2024-06-07T06:35:37Z)
• A Multi-Source Retrieval Question Answering Framework Based on RAG [3.731892340350648]
  This study proposes a method that replaces traditional retrievers with GPT-3.5. We also propose a web retrieval based method to implement fine-grained knowledge retrieval. In order to mitigate the illusion of GPT retrieval and reduce noise in Web retrieval,we proposes a multi-source retrieval framework, named MSRAG.
  arXiv  Detail & Related papers  (2024-05-29T15:47:57Z)
• Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop [65.23044868332693]
  We investigate the impact of source bias on the realm of recommender systems. We show the prevalence of source bias and reveal a potential digital echo chamber with source bias amplification. We introduce a black-box debiasing method that maintains model impartiality towards both HGC and AIGC.
  arXiv  Detail & Related papers  (2024-05-28T09:34:50Z)
• ARAGOG: Advanced RAG Output Grading [44.99833362998488]
  Retrieval-Augmented Generation (RAG) is essential for integrating external knowledge into Large Language Model (LLM) outputs. This study assesses various RAG methods' impacts on retrieval precision and answer similarity.
  arXiv  Detail & Related papers  (2024-04-01T10:43:52Z)
• Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System [40.33178881317882]
  We propose the application of maximal marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. We evaluate our approach on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models.
  arXiv  Detail & Related papers  (2023-10-13T06:03:47Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• Optimal Condition Training for Target Source Separation [56.86138859538063]
  We propose a new optimal condition training method for single-channel target source separation. We show that the complementary information carried by the diverse semantic concepts significantly helps to disentangle and isolate sources of interest.
  arXiv  Detail & Related papers  (2022-11-11T00:04:55Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)

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.