MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems

• URL: http://arxiv.org/abs/2501.03468v1
• Date: Tue, 07 Jan 2025 01:52:56 GMT
• Title: MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
• Abstract summary: We present MTRAG: an end-to-end human-generated multi-turn RAG benchmark.<n>It reflects several real-world properties across diverse dimensions for evaluating the full RAG pipeline.<n>Our evaluations show that even state-of-the-art LLM RAG systems struggle on MTRAG.
• Score: 21.554151870748104
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several additional challenges. We present MTRAG: an end-to-end human-generated multi-turn RAG benchmark that reflects several real-world properties across diverse dimensions for evaluating the full RAG pipeline. MTRAG contains 110 conversations averaging 7.7 turns each across four domains for a total of 842 tasks. We also explore automation paths via synthetic data and LLM-as-a-Judge evaluation. Our human and automatic evaluations show that even state-of-the-art LLM RAG systems struggle on MTRAG. We demonstrate the need for strong retrieval and generation systems that can handle later turns, unanswerable questions, non-standalone questions, and multiple domains. MTRAG is available at https://github.com/ibm/mt-rag-benchmark.

Related papers

• MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks [68.78064578688809]
  MTR-Suite is a unified framework for auditing, synthesizing, and benchmarking retrieval.<n>It features: (1) MTR-Eval, an LLM-based auditor quantifying alignment gaps in previous benchmarks; (2) MTR-Pipeline, a multi-agent system using greedy clustering to generate high-fidelity dialogues at 1/400th human cost; and (3) MTR-Bench, a rigorous general-domain benchmark.
  arXiv  Detail & Related papers  (2026-05-20T05:26:35Z)
• Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration [57.23877089929136]
  Graph-of-Agents (GoA) is a new graph-based framework for modeling multi-agent LLM communication.<n>GoA achieves superior performance using only 3 selected agents, outperforming recent multi-agent LLM baselines.
  arXiv  Detail & Related papers  (2026-04-18T21:13:03Z)
• ATP-Bench: Towards Agentic Tool Planning for MLLM Interleaved Generation [14.85015534787527]
  Interleaved text-and-image generation offers a more intuitive way to convey complex information.<n>Current paradigms rely on either image generation or retrieval augmentation, failing to unify factuality with creativity.<n>We argue that the next milestone in this field is Agentic Tool Planning, where the model serves as a central controller that autonomously determines when, where, and which tools to invoke to produce interleaved responses for visual-critical queries.
  arXiv  Detail & Related papers  (2026-03-31T15:47:59Z)
• Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage [89.58253972744531]
  Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation.<n>We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response's information coverage.<n>Our findings demonstrate strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses at both topic and system levels.
  arXiv  Detail & Related papers  (2026-03-09T18:20:20Z)
• MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations [11.752769691113862]
  We release a benchmark of 666 tasks containing over 2,800 conversation turns across 6 domains with accompanying corpora.<n>Our experiments show that retrieval and generation models continue to struggle on conversations with UNanswerable, UNderspecified, and NONstandalone questions and UNclear responses.
  arXiv  Detail & Related papers  (2026-02-26T16:41:17Z)
• Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA [18.46710400838861]
  This paper addresses the lack of a systematic comparison of RAG methods for multi-turn conversational QA.<n>We present a comprehensive empirical study of vanilla and advanced RAG methods across eight diverse conversational QA datasets.<n>Our results show that robust yet straightforward methods, such as reranking, hybrid BM25, and HyDE, consistently outperform vanilla RAG.
  arXiv  Detail & Related papers  (2026-02-10T08:59:23Z)
• Evaluating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-hop Queries [53.99620546358492]
  Real-world use cases often present RAG systems with complex queries for which relevant information is missing from the corpus or is incomplete.<n>Existing RAG benchmarks rarely reflect realistic task complexity for multi-hop or out-of-scope questions.<n>We present the first pipeline for automatic, difficulty-controlled creation of un$underlinec$heatable, $underliner$ealistic, $underlineu$nanswerable, and $underlinem$ulti-hop.
  arXiv  Detail & Related papers  (2025-10-13T21:38:04Z)
• MSRS: Evaluating Multi-Source Retrieval-Augmented Generation [51.717139132190574]
  Many real-world applications demand the ability to integrate and summarize information scattered across multiple sources.<n>We present a scalable framework for constructing evaluation benchmarks that challenge RAG systems to integrate information across distinct sources.
  arXiv  Detail & Related papers  (2025-08-28T14:59:55Z)
• Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations [70.94563079082751]
  E-commerce has exposed the limitations of traditional product retrieval systems in managing complex, multi-turn user interactions.<n>We propose a novel framework that introduces test-time scaling into conversational multimodal product retrieval.<n>Our approach builds on a generative retriever, further augmented with a test-time reranking mechanism that improves retrieval accuracy and better aligns results with evolving user intent throughout the dialogue.
  arXiv  Detail & Related papers  (2025-08-25T15:38:56Z)
• AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs [24.403284945948272]
  AutoJudger is an agent-driven framework for efficient and adaptive benchmarking of multimodal large language models.<n>AutoJudger employs the Item Response Theory (IRT) to estimate the question difficulty and an autonomous evaluation agent to dynamically select the most informative test questions.
  arXiv  Detail & Related papers  (2025-05-27T16:17:15Z)
• 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)
• 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)
• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [92.57125498367907]
  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. Our model, MM-Embed, achieves state-of-the-art performance on the multimodal retrieval benchmark M-BEIR.
  arXiv  Detail & Related papers  (2024-11-04T20:06:34Z)
• ScopeQA: A Framework for Generating Out-of-Scope Questions for RAG [52.33835101586687]
  Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries.<n>This paper presents a novel guided hallucination-based method to efficiently generate a diverse set of borderline out-of-scope confusing questions.
  arXiv  Detail & Related papers  (2024-10-18T16:11:29Z)
• 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)
• Toward General Instruction-Following Alignment for Retrieval-Augmented Generation [63.611024451010316]
  Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. We propose VIF-RAG, the first automated, scalable, and verifiable synthetic pipeline for instruction-following alignment in RAG systems.
  arXiv  Detail & Related papers  (2024-10-12T16:30:51Z)
• Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA [16.1357049130957]
  We build on the single-turn SELF-RAG framework and propose SELF-multi-RAG for conversational settings. SELF-multi-RAG demonstrates improved capabilities over single-turn variants with respect to retrieving relevant passages.
  arXiv  Detail & Related papers  (2024-09-23T20:05:12Z)
• 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)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• BERGEN: A Benchmarking Library for Retrieval-Augmented Generation [26.158785168036662]
  Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline. In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments.
  arXiv  Detail & Related papers  (2024-07-01T09:09:27Z)
• 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 [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)
• ERATTA: Extreme RAG for Table To Answers with Large Language Models [1.3318204310917532]
  Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions. We propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user-query routing, data-retrieval and custom prompting for question-answering capabilities from Enterprise-data tables. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains.
  arXiv  Detail & Related papers  (2024-05-07T02:49:59Z)

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.