ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator

• URL: http://arxiv.org/abs/2405.18111v3
• Date: Tue, 08 Oct 2024 06:37:03 GMT
• Title: ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
• Abstract summary: Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) We propose to optimize the retrieval-augmented Generator with an Adversarial Tuning Multi-agent system (ATM)
• Score: 22.81784849762196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions. RAG adopts information retrieval techniques to inject external knowledge from semantic-relevant documents as input contexts. However, since today's Internet is flooded with numerous noisy and fabricating content, it is inevitable that RAG systems are vulnerable to these noises and prone to respond incorrectly. To this end, we propose to optimize the retrieval-augmented Generator with an Adversarial Tuning Multi-agent system (ATM). The ATM steers the Generator to have a robust perspective of useful documents for question answering with the help of an auxiliary Attacker agent through adversarially tuning the agents for several iterations. After rounds of multi-agent iterative tuning, the Generator can eventually better discriminate useful documents amongst fabrications. The experimental results verify the effectiveness of ATM and we also observe that the Generator can achieve better performance compared to the state-of-the-art baselines.

Related papers

• MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation [36.73029890123542]
  MASS-RAG is a multi-agent synthesis approach to retrieval-augmented generation.<n>It applies distinct agents for evidence summarization, evidence extraction, and reasoning over retrieved documents.<n>It combines their outputs through a dedicated synthesis stage to produce the final answer.
  arXiv  Detail & Related papers  (2026-04-20T17:00:38Z)
• AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition [62.16431420189863]
  LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states.<n>We introduce AffectAgent, an affect-oriented multi-agent retrieval-augmented generation framework.<n>AffectAgent comprises three jointly optimized agents, namely a query planner, an evidence filter, and an emotion generator.
  arXiv  Detail & Related papers  (2026-04-14T13:49:19Z)
• Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation [64.09110141948693]
  Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise.<n>We propose BAR-RAG, which reframes the reranker as a boundary-aware evidence selector that targets the generator's Goldilocks Zone.<n>Bar-RAG consistently improves end-to-end performance under noisy retrieval.
  arXiv  Detail & Related papers  (2026-02-03T16:08:23Z)
• 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)
• HV-Attack: Hierarchical Visual Attack for Multimodal Retrieval Augmented Generation [30.52562642784609]
  multimodal Retrieval-Augmented Generation (MRAG) techniques have been widely applied to enhance the capabilities of Large Multimodal Models (LMMs)<n>Existing adversarial research has revealed the vulnerability of MRAG systems to knowledge poisoning attacks.<n>We propose visual attack of MRAG by solely adding imperceptible perturbations at the image inputs of users.
  arXiv  Detail & Related papers  (2025-11-19T13:45:24Z)
• Query Decomposition for RAG: Balancing Exploration-Exploitation [83.79639293409802]
  RAG systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggregating them to generate an answer.<n>We formulate query decomposition and document retrieval in an exploitation-exploration setting, where retrieving one document at a time builds a belief about the utility of a given sub-queries.<n>Our main finding is that estimating document relevance using rank information and human judgments yields a 35% gain in document-level precision, 15% increase in alpha-nDCG, and better performance on the downstream task of long-form generation.
  arXiv  Detail & Related papers  (2025-10-21T13:37:11Z)
• Enhancing Retrieval Augmentation via Adversarial Collaboration [50.117273835877334]
  We propose the Adrial Collaboration RAG (AC-RAG) framework to address "Retrieval Hallucinations"<n>AC-RAG employs two heterogeneous agents: a generalist Detector that identifies knowledge gaps, and a domain-specialized Resolver that provides precise solutions.<n>Experiments show that AC-RAG significantly improves retrieval accuracy and outperforms state-of-the-art RAG methods across various vertical domains.
  arXiv  Detail & Related papers  (2025-09-18T08:54:20Z)
• SIRAG: Towards Stable and Interpretable RAG with A Process-Supervised Multi-Agent Framework [7.37561751991963]
  We propose a process-supervised multi-agent framework to bridge the gap between retriever and generator.<n>The proposed framework is modular and plug-and-play, requiring no modification to the retriever or generator.
  arXiv  Detail & Related papers  (2025-09-17T09:09:28Z)
• HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering [25.01360941843264]
  HANRAG is a novel framework designed to efficiently tackle problems of varying complexity.<n> Driven by a powerful revelator, HANRAG routes queries, decomposes them into sub-queries, and filters noise from retrieved documents.<n>Results demonstrate that our framework obtains superior performance in both single-hop and multi-hop question-answering tasks.
  arXiv  Detail & Related papers  (2025-09-08T06:22:38Z)
• AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generation [0.5013248430919223]
  AutoBnB-RAG is an extension of the AutoBnB framework that incorporates retrieval-augmented generation (RAG) into multi-agent incident response simulations.<n>Built on the Backdoors & Breaches (B&B) tabletop game environment, AutoBnB-RAG enables agents to issue retrieval queries and incorporate external evidence during collaborative investigations.
  arXiv  Detail & Related papers  (2025-08-18T17:22:51Z)
• 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)
• Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios [76.02688769599686]
  We propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC.<n>Specifically, we reformulate noisy identification as an anomaly identification problem using GMM.<n>We then design a hybrid rectification strategy to mitigate the adverse effects of noisy data based on the identification results.
  arXiv  Detail & Related papers  (2025-05-27T16:16:54Z)
• MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning [43.66966457772646]
  MA-RAG orchestrates a collaborative set of specialized AI agents to tackle each stage of the RAG pipeline with task-aware reasoning.<n>Our design allows fine-grained control over information flow without any model fine-tuning.<n>This modular and reasoning-driven architecture enables MA-RAG to deliver robust, interpretable results.
  arXiv  Detail & Related papers  (2025-05-26T15:05:18Z)
• Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps [16.84310001807895]
  This paper introduces a model-agnostic approach that can be applied to A-RAG methods.<n>Specifically, we use cache access and parallel generation to speed up the prefilling and decoding stages respectively.
  arXiv  Detail & Related papers  (2025-05-19T05:39:38Z)
• DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation [23.060355911225923]
  Reranker plays vital role in refining retrieved documents to enhance generation quality and explainability.<n>We propose DynamicRAG, a novel RAG framework where the reranker dynamically adjusts both the order and number of retrieved documents.
  arXiv  Detail & Related papers  (2025-05-12T05:19:01Z)
• Reinforced Information Retrieval [35.0424269986952]
  We present textbfReinforced-IR, a novel approach that jointly adapts a pre-trained retriever and generator for precise cross-domain retrieval. A key innovation of Reinforced-IR is its textbfSelf-Boosting framework, which enables retriever and generator to learn from each other's feedback. In our experiment, Reinforced-IR outperforms existing domain adaptation methods by a large margin, leading to substantial improvements in retrieval quality across a wide range of application scenarios.
  arXiv  Detail & Related papers  (2025-02-17T08:52:39Z)
• MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation [34.66546005629471]
  Large Language Models (LLMs) are essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information. Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses. To tackle this problem, we propose Multi-Agent Filtering Retrieval-Augmented Generation (MAIN-RAG) MAIN-RAG is a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents.
  arXiv  Detail & Related papers  (2024-12-31T08:07:26Z)
• PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization [35.48003039415176]
  Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in large language models (LLMs) RAG generators often suffer from inadequate response informativeness, response robustness, and citation quality. We propose Multiple Perspective Preference Alignment for Retrieval-Augmented Generation (PA-RAG) to align with RAG requirements comprehensively.
  arXiv  Detail & Related papers  (2024-12-19T04:18:51Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively. We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Using Generative AI and Multi-Agents to Provide Automatic Feedback [4.883570605293337]
  This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the quality of GenAI-generated feedback.
  arXiv  Detail & Related papers  (2024-11-11T22:27:36Z)
• Self-adaptive Multimodal Retrieval-Augmented Generation [0.0]
  We propose a new approach called Self-adaptive Multimodal Retrieval-Augmented Generation (SAM-RAG) SAM-RAG not only dynamically filters relevant documents based on the input query, including image captions when needed, but also verifies the quality of both the retrieved documents and the output. Extensive experimental results show that SAM-RAG surpasses existing state-of-the-art methods in both retrieval accuracy and response generation.
  arXiv  Detail & Related papers  (2024-10-15T06:39:35Z)
• 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)
• 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 51% compared to conventional RAG systems on PubHealth.
  arXiv  Detail & Related papers  (2024-07-11T06:50:19Z)
• Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models [21.115495457454365]
  uRAG is a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. We build a large-scale experimentation ecosystem consisting of 18 RAG systems that engage in training and 18 unknown RAG systems that use the uRAG as the new users of the search engine.
  arXiv  Detail & Related papers  (2024-04-30T19:51:37Z)
• Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions [89.35345649303451]
  Generative search engines have the potential to transform how people seek information online. But generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system.
  arXiv  Detail & Related papers  (2024-02-25T11:22:19Z)
• Corrective Retrieval Augmented Generation [36.04062963574603]
  Retrieval-augmented generation (RAG) relies heavily on relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. We propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches.
  arXiv  Detail & Related papers  (2024-01-29T04:36:39Z)
• CCA: Collaborative Competitive Agents for Image Editing [59.54347952062684]
  This paper presents a novel generative model, Collaborative Competitive Agents (CCA) It leverages the capabilities of multiple Large Language Models (LLMs) based agents to execute complex tasks. The paper's main contributions include the introduction of a multi-agent-based generative model with controllable intermediate steps and iterative optimization.
  arXiv  Detail & Related papers  (2024-01-23T11:46:28Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)
• 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)
• Generation-Augmented Retrieval for Open-domain Question Answering [134.27768711201202]
  Generation-Augmented Retrieval (GAR) for answering open-domain questions. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader.
  arXiv  Detail & Related papers  (2020-09-17T23:08:01Z)

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.