CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation (2410.23090v1)

Abstract: Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing LLMs through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world applications. To bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia and tackles key challenges such as open-domain coverage, knowledge intensity, free-form responses, and topic shifts. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. We propose a unified framework to standardize various conversational RAG methods and conduct a comprehensive evaluation of these methods on CORAL, demonstrating substantial opportunities for improving existing approaches.

• The paper introduces CORAL, a benchmark that evaluates multi-turn conversational RAG systems using realistic, Wikipedia-derived dialogues.
• It outlines a structured methodology with sampling strategies like LDS and DTRW to simulate complex conversational shifts.
• Experiments reveal that while response quality plateaus with larger models, citation accuracy benefits from increased model scaling.

An Examination of "CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation"

The paper of Retrieval-Augmented Generation (RAG) systems has seen considerable advancements in recent years, especially with their integration with LLMs for enhanced response quality in question-answering tasks. Nonetheless, academic evaluation has largely emphasized single-turn interactions, thereby neglecting the complexities of multi-turn conversations prevalent in realistic settings. The paper "CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation" introduces CORAL, a novel benchmark explicitly designed for evaluating RAG systems within multi-turn conversational contexts. This benchmark represents a significant stride towards bridging the existing gap between laboratory conditions and real-world applications in conversational AI.

CORAL derives its dataset from information-seeking dialogues generated from Wikipedia, ensuring comprehensive coverage across several dimensions critical for robust RAG evaluations. The key features of CORAL include open-domain coverage, knowledge-intensive inquiries, facilitation of free-form response generation, adaptation to topic shifts, and the provision of citation labeling. This compositional focus sets it apart from conventional datasets, addressing the multifaceted challenges that exist in multi-turn conversations.

The paper details a systematic methodology for converting raw Wikipedia content into a structured format suitable for evaluating conversational RAG systems. By leveraging the hierarchical structure of Wikipedia pages, the authors create complex informational flows that mimic genuine conversational shifts and dependencies. The benchmark includes 8,000 conversations, rigorously sampled and classified into types based on various sampling strategies such as Linear Descent Sampling (LDS) and Dual-Tree Random Walk (DTRW). These strategies explore different depths, breadths, and topical diversities of conversations, offering a realistic dataset for RAG evaluation.

CORAL supports three essential tasks: Conversational Passage Retrieval, Response Generation, and Citation Labeling, which collectively cover the primary functionalities required for optimal RAG system performance in real-world settings. The proposed unified framework standardizes the assessment of various conversational RAG approaches, thus facilitating a cohesive comparison across different methods.

The paper's experiments provide insights into the current efficacy and limitations of RAG systems when applied to multi-turn interactions. Evaluations using both open-source and commercial LLMs reveal opportunities for further refinement, especially in the context of citation accuracy and response sophistication. The deployment of alternative conversation compression strategies—such as LLM-based summarization of conversation history—demonstrates practical ways to mitigate the long context problem often encountered in extended dialogue history scenarios.

This research also offers a valuable discourse on the implications of model scaling. With an extensive analysis covering parameter scaling from 500 million to 7 billion, the authors highlight that while response quality tends to plateau beyond a certain model size, citation accuracy benefits from larger models, suggesting that different facets of conversational RAG systems might optimize at differing scales.

In conclusion, CORAL fulfills a critical need for comprehensive evaluation in conversational RAG, providing a versatile benchmark capable of advancing multi-turn dialogue systems towards practical use. The authors’ contributions lay a foundation that future research can build upon, notably in refining methods for context handling and response generation within dynamic, information-rich conversation settings. Future work will likely explore the integration of more sophisticated retrieval and generation architectures, as well as refining the simulation of complex conversational nuances further, contributing to the broader field of AI's practical deployment in interactive systems.