AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning

Understand

Despite the outstanding capabilities of large language models (LLMs), knowledge-intensive reasoning still remains a challenging task due to LLMs' limitations in compositional reasoning and the hallucination problem.

• A prevalent solution is to employ chain-of-thought (CoT) with retrieval-augmented generation (RAG), which first formulates a reasoning plan by decomposing complex questions into simpler sub-questions, and then applies iterative RAG at each sub-question.
• However, prior works exhibit two crucial problems: inadequate reasoning planning and poor incorporation of heterogeneous knowledge.
• In this paper, we introduce AtomR, a framework for LLMs to conduct accurate heterogeneous knowledge reasoning at the atomic level.