Summary of Grammar: Grounded and Modular Methodology For Assessment Of Closed-domain Retrieval-augmented Language Model, by Xinzhe Li et al.
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language Model
by Xinzhe Li, Ming Liu, Shang Gao
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed GRAMMAR framework is an innovative evaluation methodology for Retrieval-Augmented Generation (RAG) systems, specifically designed to address the challenges of evaluating RAG systems querying closed-domain knowledge bases. The framework comprises a grounded data generation process and an evaluation protocol that diagnoses problematic modules and identifies types of failure, including those caused by knowledge deficits or robustness issues. GRAMMAR provides a reliable approach for identifying vulnerable modules and supports hypothesis testing for textual form vulnerabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG systems are used to query closed-domain knowledge bases across various industries. Evaluating these systems is challenging due to the private nature of the data and limited queries with verifiable answers. Researchers developed an evaluation framework called GRAMMAR to solve this problem. The framework helps identify weak parts of the system and figure out why they’re not working well. By using this framework, experts can test hypotheses about how RAG systems work and make improvements. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
