A framework called Micro-Act addresses Knowledge Conflicts in Retrieval-Augmented Generation by adaptively decomposing knowledge sources, leading to improved QA accuracy compared to existing methods.
Retrieval-Augmented Generation (RAG) systems commonly suffer from Knowledge
Conflicts, where retrieved external knowledge contradicts the inherent,
parametric knowledge of large language models (LLMs). It adversely affects
performance on downstream tasks such as question answering (QA). Existing
approaches often attempt to mitigate conflicts by directly comparing two
knowledge sources in a side-by-side manner, but this can overwhelm LLMs with
extraneous or lengthy contexts, ultimately hindering their ability to identify
and mitigate inconsistencies. To address this issue, we propose Micro-Act a
framework with a hierarchical action space that automatically perceives context
complexity and adaptively decomposes each knowledge source into a sequence of
fine-grained comparisons. These comparisons are represented as actionable
steps, enabling reasoning beyond the superficial context. Through extensive
experiments on five benchmark datasets, Micro-Act consistently achieves
significant increase in QA accuracy over state-of-the-art baselines across all
5 datasets and 3 conflict types, especially in temporal and semantic types
where all baselines fail significantly. More importantly, Micro-Act exhibits
robust performance on non-conflict questions simultaneously, highlighting its
practical value in real-world RAG applications.