MAFA: A multi-agent framework for annotation

• URL: http://arxiv.org/abs/2505.13668v1
• Date: Mon, 19 May 2025 19:16:37 GMT
• Title: MAFA: A multi-agent framework for annotation
• Authors: Mahmood Hegazy, Aaron Rodrigues, Azzam Naeem,
• Abstract summary: We introduce a multi-agent framework for annotation that combines multiple specialized agents with different approaches and a judge agent that reranks candidates to produce optimal results.<n>Our framework is particularly effective at handling ambiguous queries, making it well-suited for deployment in production applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Modern applications require accurate and efficient retrieval of information in response to user queries. Mapping user utterances to the most relevant Frequently Asked Questions (FAQs) is a crucial component of these systems. Traditional approaches often rely on a single model or technique, which may not capture the nuances of diverse user inquiries. In this paper, we introduce a multi-agent framework for FAQ annotation that combines multiple specialized agents with different approaches and a judge agent that reranks candidates to produce optimal results. Our agents utilize a structured reasoning approach inspired by Attentive Reasoning Queries (ARQs), which guides them through systematic reasoning steps using targeted, task-specific JSON queries. Our framework features a specialized few-shot example strategy, where each agent receives different few-shots, enhancing ensemble diversity and coverage of the query space. We evaluate our framework on a real-world banking dataset as well as public benchmark datasets (LCQMC and FiQA), demonstrating significant improvements over single-agent approaches across multiple metrics, including a 14% increase in Top-1 accuracy, an 18% increase in Top-5 accuracy, and a 12% improvement in Mean Reciprocal Rank on our dataset, and similar gains on public benchmarks when compared with traditional single agent annotation techniques. Our framework is particularly effective at handling ambiguous queries, making it well-suited for deployment in production applications while showing strong generalization capabilities across different domains and languages.

Related papers

• Visual Document Understanding and Question Answering: A Multi-Agent Collaboration Framework with Test-Time Scaling [83.78874399606379]
  We propose MACT, a Multi-Agent Collaboration framework with Test-Time scaling.<n>It comprises four distinct small-scale agents, with clearly defined roles and effective collaboration.<n>It shows superior performance with a smaller parameter scale without sacrificing the ability of general and mathematical tasks.
  arXiv  Detail & Related papers  (2025-08-05T12:52:09Z)
• Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks [56.350173737493215]
  Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency.<n>MMESGBench is a first-of-its-kind benchmark dataset to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents.<n>MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories.
  arXiv  Detail & Related papers  (2025-07-25T03:58:07Z)
• GENIUS: A Generative Framework for Universal Multimodal Search [26.494338650656594]
  This paper proposes GENIUS, a universal generative retrieval framework supporting diverse tasks across multiple modalities and domains.<n>At its core, GENIUS introduces modality-decoupled semantic quantization, transforming multimodal data into discrete IDs encoding both modality and semantics.<n>To enhance generalization, we propose a query augmentation that interpolates between a query and its target, allowing GENIUS to adapt to varied query forms.
  arXiv  Detail & Related papers  (2025-03-25T17:32:31Z)
• Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning [76.10639521319382]
  We propose Symbolic-MoE, a symbolic, text-based, and gradient-free Mixture-of-Experts framework.<n>We show that Symbolic-MoE's instance-level expert selection improves performance by a large margin but -- when implemented naively -- can introduce a high computational overhead.
  arXiv  Detail & Related papers  (2025-03-07T18:03:13Z)
• Agent-centric Information Access [21.876205078570507]
  Large language models (LLMs) become more specialized, each trained on proprietary data and excelling in specific domains.<n>This paper introduces a framework for agent-centric information access, where LLMs function as knowledge agents that are dynamically ranked and queried based on their demonstrated expertise.<n>We propose a scalable evaluation framework that leverages retrieval-augmented generation and clustering techniques to construct and assess thousands of specialized models, with the potential to scale toward millions.
  arXiv  Detail & Related papers  (2025-02-26T16:56:19Z)
• HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks [50.871243190126826]
  HawkBench is a human-labeled, multi-domain benchmark designed to rigorously assess RAG performance.<n>By stratifying tasks based on information-seeking behaviors, HawkBench provides a systematic evaluation of how well RAG systems adapt to diverse user needs.
  arXiv  Detail & Related papers  (2025-02-19T06:33:39Z)
• QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance [1.433758865948252]
  This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems.<n>RAG architecture is constructed to generate responses from the target document.<n>We introduce QuIM-RAG, a novel approach for the retrieval mechanism in our system.
  arXiv  Detail & Related papers  (2025-01-06T01:07:59Z)
• A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data [0.0]
  Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs)<n>Traditional RAG systems typically use a single-agent architecture to handle query generation, data retrieval, and response synthesis.<n>This paper proposes a multi-agent RAG system to address these limitations.
  arXiv  Detail & Related papers  (2024-12-08T07:18:19Z)
• A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning [34.85351481228439]
  We propose a unified coordinating framework to efficiently utilize existing ML-based agents. We show that it can effectively utilize different ML-based agents and find better configurations with 1.414.1X speedups on the workload execution time.
  arXiv  Detail & Related papers  (2023-03-10T05:27:23Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)
• RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering [57.94658176442027]
  We present RnG-KBQA, a Rank-and-Generate approach for KBQA. We achieve new state-of-the-art results on GrailQA and WebQSP datasets.
  arXiv  Detail & Related papers  (2021-09-17T17:58:28Z)
• Text Summarization with Latent Queries [60.468323530248945]
  We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
  arXiv  Detail & Related papers  (2021-05-31T21:14:58Z)

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.