InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Understand

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding responses with retrieved information.

• As an emerging paradigm, Agentic RAG further enhances this process by introducing autonomous LLM agents into the information seeking process.
• However, existing benchmarks fall short in evaluating such systems, as they are confined to a static retrieval environment with a fixed, limited corpus} and simple queries that fail to elicit agentic behavior.
• Moreover, their evaluation protocols assess information seeking effectiveness by pre-defined gold sets of documents, making them unsuitable for the open-ended and dynamic nature of real-world web environments.