Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track (2406.16828v1)

Abstract: Did you try out the new Bing Search? Or maybe you fiddled around with Google AI~Overviews? These might sound familiar because the modern-day search stack has recently evolved to include retrieval-augmented generation (RAG) systems. They allow searching and incorporating real-time data into LLMs to provide a well-informed, attributed, concise summary in contrast to the traditional search paradigm that relies on displaying a ranked list of documents. Therefore, given these recent advancements, it is crucial to have an arena to build, test, visualize, and systematically evaluate RAG-based search systems. With this in mind, we propose the TREC 2024 RAG Track to foster innovation in evaluating RAG systems. In our work, we lay out the steps we've made towards making this track a reality -- we describe the details of our reusable framework, Ragnar\"ok, explain the curation of the new MS MARCO V2.1 collection choice, release the development topics for the track, and standardize the I/O definitions which assist the end user. Next, using Ragnar\"ok, we identify and provide key industrial baselines such as OpenAI's GPT-4o or Cohere's Command R+. Further, we introduce a web-based user interface for an interactive arena allowing benchmarking pairwise RAG systems by crowdsourcing. We open-source our Ragnar\"ok framework and baselines to achieve a unified standard for future RAG systems.

Overview of Ragnar: A Reusable RAG Framework and Baselines for TREC 2024 RAG Track

The article presents "Ragnar," a reusable framework designed for Retrieval-Augmented Generation (RAG) systems, alongside its standard datasets and evaluation metrics tailored for the TREC 2024 RAG track. The proposed framework aims to facilitate the development, benchmarking, and evaluation of RAG systems—a paradigm vital for enhancing the effectiveness of search engines and AI systems in generating coherent, contextually grounded responses by leveraging real-time data.

Key Contributions

1. Ragnar Framework:
   • Structure: The framework integrates two primary components: the Retrieval (R) and Augmented Generation (AG) modules. The retrieval module employs various state-of-the-art retrievers and rerankers, while the AG module harnesses LLMs to generate responses grounded on the retrieved segments.
   • Flexibility and Integration: The system seamlessly interacts with well-established retrieval frameworks like Pyserini and RankLLM, and provides REST APIs and a web-based interface for user interaction.
2. Datasets:
   • MS MARCO V2.1 Collection: A deduplicated version of the MS MARCO V2 document collection, which reduces redundancies and enhances the richness of information retrieval.
   • Topic Collections: Two distinct topic sets are introduced—TREC-RAGgy 2024, derived from TREC Deep Learning tracks and designed to foster the formulation of long-form answers, and TREC-Researchy 2024, based on Bing Search logs covering knowledge-intensive and multifaceted queries.
3. Evaluation Methodology:
   • Baseline Systems: The paper benchmarks industrial-grade models like Cohere's Command R+ and OpenAI's GPT-4o against the proposed Ragnar framework, offering a robust comparative analysis.
   • RAG-Bench: An innovative evaluation arena inspired by tools like Chatbot Arena, where multiple RAG systems can be pitted head-to-head for comparative human and automated evaluation.

Implications and Future Work

The development of the Ragnar framework and associated datasets has practical and theoretical ramifications for the field of Information Retrieval and Natural Language Processing. Practically, the framework paves the way for a standardized evaluation and comparison of RAG systems, which is paramount for their deployment in real-world applications like search engines and virtual assistants.

Theoretically, this research compels further investigation into more refined RAG methods. Potential future developments could include:

• Advanced Retrieval Models: Incorporating dual encoder models and newer variants like Artic-Embed, which could enhance initial retrieval precision.
• Enhanced RAG Techniques: Adoption of sophisticated techniques like SelfRAG and CRAG, improving the synergy between retrieval and generation stages.
• Evaluation Metrics: Establishing advanced evaluation methodologies, possibly leveraging nugget-based evaluation strategies to enhance granularity and fairness in system assessment.

The presented Ragnar framework emphasizes reproducibility and accessibility, providing open-source code and clear input/output definitions. This transparency is crucial for ensuring that advancements in RAG systems are built on a mutual foundation of validated methodologies.

Overall, the work's structured approach in presenting a cohesive framework, comprehensive datasets, and robust evaluation strategies provides a solid benchmark for the upcoming TREC 2024 RAG track and sets a standard for future research in the area of Retrieval-Augmented Generation.

Conclusion

This paper introduces Ragnar, a pivotal framework facilitating the development and evaluation of RAG systems within the TREC 2024 track. By integrating advanced retrieval and generation models, curating large-scale, diverse datasets and implementing meticulous evaluation metrics, the authors contribute substantively to both the theoretical and practical landscape of RAG systems. The implications of this work are vast, offering foundational tools and insights that will undoubtedly spur further research and development in this burgeoning field.