RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot Introduction to RAG Evaluation Issues 00:00
The video discusses the current state of Retrieval-Augmented Generation (RAG) evaluation, suggesting it is overhyped and not fully resolved.
Yuval and Niv from A21 labs outline the primary issues with RAG evaluations, focusing on the limitations of existing benchmarks.
Problems with Current Benchmarks 00:16
Existing benchmarks often rely on local questions with local answers, leading to unrealistic testing environments.
Some benchmarks, like Google's, create overly complex questions that do not reflect real-world scenarios.
Many benchmarks are either retrieval-only or generation-only, lacking a holistic approach to evaluate RAG systems effectively.
Flawed Benchmarking Cycle 02:11
There exists a vicious cycle where RAG systems are developed based on flawed benchmarks, leading to inflated performance claims.
High scores on these benchmarks do not translate to real-world effectiveness, causing discrepancies when tested with actual user data.
Examples of Problematic Questions 03:13
The presenters illustrate how RAG systems struggle with aggregative and counting questions in financial data or sports statistics.
They highlight the limitations of the systems when responding to complex queries that require synthesizing information from multiple sources.
Proposed Solutions for RAG Evaluation 05:24
The presenters suggest converting unstructured corpuses into a more organized data structure to handle complex queries more effectively.
They advocate for using SQL-like structures to process questions rather than relying on traditional RAG methods.
Implementation Strategy 06:35
The proposed method involves clustering documents into sub-corpuses and defining schemas for each, which enhances data retrieval efficiency.
The ingestion phase is critical, where the schema is populated for each document, allowing for better query responses.
Challenges and Limitations 08:13
Not all data sets can be easily structured into relational databases, and normalization of data is a significant challenge.
The presenters acknowledge that the approach may not solve all issues, particularly with complex queries or heterogeneous data.
Key Takeaways 09:58
RAG systems should be tailored to individual client needs rather than applying a one-size-fits-all approach.
Current evaluation benchmarks fail to capture the complexities of real-world applications, necessitating new evaluation frameworks for specific use cases.