A Reproducibility Study of Metacognitive Retrieval-Augmented Generation

TL;DR

This paper reproduces and extends MetaRAG, a retrieval system for large language models that uses metacognition to improve reasoning, highlighting its benefits and challenges in practical implementation.

Contribution

It confirms MetaRAG's improvements over standard RAG, evaluates rerankers, compares with SIM-RAG, and discusses reproducibility challenges and robustness enhancements.

Findings

MetaRAG shows relative improvements over standard RAG.

Reranking significantly boosts MetaRAG performance.

MetaRAG is more robust than SIM-RAG with additional retrieval features.

Abstract

Recently, Retrieval Augmented Generation (RAG) has shifted focus to multi-retrieval approaches to tackle complex tasks such as multi-hop question answering. However, these systems struggle to decide when to stop searching once enough information has been gathered. To address this, \citet{zhou2024metacognitive} introduced Metacognitive Retrieval Augmented Generation (MetaRAG), a framework inspired by metacognition that enables Large Language Models to critique and refine their reasoning. In this reproducibility paper, we reproduce MetaRAG following its original experimental setup and extend it in two directions: (i) by evaluating the effect of PointWise and ListWise rerankers, and (ii) by comparing with SIM-RAG, which employs a lightweight critic model to stop retrieval. Our results confirm MetaRAG's relative improvements over standard RAG and reasoning-based baselines, but also reveal lower absolute scores than reported, reflecting challenges with closed-source LLM updates, missing implementation details, and unreleased prompts. We show that MetaRAG is partially reproduced, gains substantially from reranking, and is more robust than SIM-RAG when extended with additional retrieval features.