Generating Q&A Benchmarks for RAG Evaluation in Enterprise Settings (2025.acl-industry)
Copied to clipboard
| Challenge: | DataMorgana generates synthetic Q&A benchmarks tailored to RAG applications . lexical, syntactic, and semantic diversity of generated benchmarks exceeds existing tools . |
| Approach: | They propose a tool for generating synthetic Q&A benchmarks tailored to RAG applications in enterprise settings. |
| Outcome: | The proposed tool surpasses existing tools in terms of lexical, syntactic, and semantic diversity while maintaining high quality. |
Similar Papers
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2025.coling-industry)
Copied to clipboard
Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
| Challenge: | Retrieval Augmented Generation (RAG) systems are widespread in the industry. |
| Approach: | They propose to use Q&A datasets to assess retrieval performance and label-targeted data generation to refine RAG datasets. |
| Outcome: | The proposed system can generate Q&A datasets with fine-tuned small LLMs. |
DRAGOn: Designing RAG On Periodically Updated Corpus (2026.eacl-srw)
Copied to clipboard
Fedor Chernogorskii, Sergei Averkiev, Liliya Kudraleeva, Zaven Martirosian, Maria Tikhonova, Valentin Malykh, Alena Fenogenova
| Challenge: | Existing methods for evaluating RAG systems are labor-intensive and difficult to maintain. |
| Approach: | They propose a method to design a RAG benchmark on a regularly updated corpus. |
| Outcome: | The proposed method uses a regularly updated corpus to evaluate RAG models. |
Benchmarking Deep Search over Heterogeneous Enterprise Data (2025.emnlp-industry)
Copied to clipboard
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath, Kung-Hsiang Huang, Caiming Xiong, Chien-Sheng Wu
| Challenge: | Existing methods struggle to conduct deep searches and retrieve all necessary evidence. |
| Approach: | They propose a benchmark for evaluating deep search, a retrieval-augmented generation that requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources. |
| Outcome: | The proposed benchmarks show that even the best-performing agentic RAG methods achieve an average performance score of 32.96 on the benchmark. |
T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing QA datasets containing text-and-table data typically contain context-dependent questions, which may yield multiple correct answers depending on the provided context. |
| Approach: | They propose a benchmark to evaluate RAG methods on text-and-table data. |
| Outcome: | The proposed method evaluates RAG methods on real-world text-and-table data. |
ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios (2026.acl-long)
Copied to clipboard
António Loison, Quentin Macé, Antoine Edy, Victor Xing, Tom Balough, Gabriel de Souza P. Moreira, Bo Liu, Manuel Faysse, Celine Hudelot, Gautier Viaud
| Challenge: | Existing benchmarks focus on textual data, single-document comprehension, or evaluating retrieval and generation in isolation. |
| Approach: | They propose a multimodal RAG benchmark featuring multi-type queries over visually rich document corpora. |
| Outcome: | The proposed benchmark outperforms existing benchmarks in visual retrieval and human-verified queries. |
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation (2026.findings-eacl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is a common technique for grounding language models in domain-specific information. |
| Approach: | They propose a new retrieval technique that incorporates diversity into the retrieval step to improve performance on reasoning-intensive QA benchmarks. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive QA benchmarks by 4–10%. |
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems (2024.naacl-long)
Copied to clipboard
| Challenge: | Evaluating retrieval-augmented generation systems relies on hand annotations for input queries, passages to retrieve, and responses to generate. |
| Approach: | They propose an automated evaluation framework for retrieval-augmented generation (RAG) ARES fine tunes lightweight LLM judges on synthetically generated queries and answers . |
| Outcome: | The proposed framework evaluates RAG systems using only human annotations . it can be used to improve system understanding and create targeted solutions . |
RAGthoven: A Configurable Toolkit for RAG-enabled LLM Experimentation (2025.coling-demos)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have significantly altered the landscape of Natural Language Processing (NLP), but their use as a baseline method has not been extensive. |
| Approach: | They propose a tool for automatic evaluation of RAG-based pipelines that provides a simple yet powerful abstraction. |
| Outcome: | The proposed tool provides an automatic evaluation of RAG-based pipelines. |
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation (2024.findings-emnlp)
Copied to clipboard
David Rau, Hervé Déjean, Nadezhda Chirkova, Thibault Formal, Shuai Wang, Stéphane Clinchant, Vassilina Nikoulina
| Challenge: | Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. |
| Approach: | They propose a library that allows to benchmark and standardize RAG experiments. |
| Outcome: | The proposed library is an end-to-end library for reproducible research standardizing RAG experiments. |
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have improved the open-domain QA’s performance, but how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still not studied for industrial applications. |
| Approach: | They propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance. |
| Outcome: | The proposed framework achieves superior results on two kinds of QA tasks. |