Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 25·8 min read
FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
TL;DR
Existing benchmarks for retrieval-augmented generation (RAG)—systems that fetch external documents to improve AI accuracy—only test individual components, not end-to-end performance on mixed data types. FATHOMS-RAG is a 93-question benchmark with phrase-level recall metrics and hallucination detection that evaluates RAG pipelines across text, tables, images, and cross-document reasoning.
✦ Why It Matters
Engineers can now benchmark complete RAG systems on realistic multimodal tasks rather than isolated components, enabling data-driven selection between open and closed-source solutions.
Key Takeaways
- Evaluate RAG systems using the FATHOMS-RAG benchmark.
- Utilize the provided dataset for testing multimodal capabilities.
- Implement phrase-level recall metrics for accuracy assessment.
- Consider closed-source models for better performance in complex tasks.
How It Works
FATHOMS-RAG evaluates RAG systems by analyzing their ability to ingest and reason with diverse data types. It employs a dataset of questions that require the integration of text, tables, and images, allowing for a comprehensive assessment of the system's retrieval and reasoning capabilities.