  Answering Questions over Knowledge Graphs (KGQA) is key to well-functioning
autonomous language agents in various real-life applications. To improve the
neural-symbolic reasoning capabilities of language agents powered by Large
Language Models (LLMs) in KGQA, we propose the DecompositionAlignment-Reasoning
Agent (DARA) framework. DARA effectively parses questions into formal queries
through a dual mechanism: high-level iterative task decomposition and low-level
task grounding. Importantly, DARA can be efficiently trained with a small
number of high-quality reasoning trajectories. Our experimental results
demonstrate that DARA fine-tuned on LLMs (e.g. Llama-2-7B, Mistral) outperforms
both in-context learning-based agents with GPT-4 and alternative fine-tuned
agents, across different benchmarks in zero-shot evaluation, making such models
more accessible for real-life applications. We also show that DARA attains
performance comparable to state-of-the-art enumerating-and-ranking-based
methods for KGQA.
