FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
arXiv:2510.08945v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipeline's ability to ingest, retrieve, and reason about several modalities of information, differentiating it from existing benchmarks that focus on particular aspects such as retrieval. We present (1) a small, human-created dataset of 93 questions designed to evaluate a pipeline's ability to ingest textual data, tables, im
The proliferation of RAG systems in LLMs necessitates robust evaluation frameworks to benchmark their effectiveness across modalities, especially as they become more integrated into critical applications.
This benchmark provides a more comprehensive method for assessing multimodal RAG pipelines, which is crucial for improving the reliability and utility of AI systems that rely on complex data inputs.
The ability to inges t, retrieve, and reason across text, tables, and images within RAG systems can now be evaluated holistically, moving beyond siloed assessments of individual components.
- · AI developers
- · Enterprises deploying RAG
- · Multimodal AI research
- · Single-modality RAG benches
- · LLM developers without strong RAG
- · Systems with poor data ingestion
Increased focus on end-to-end RAG pipeline optimization for multimodal data.
Accelerated development of more robust and less hallucination-prone AI applications capable of handling complex, real-world information.
Potential for new product categories in AI tooling centered around multimodal RAG evaluation and monitoring.
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Read at arXiv cs.AI