\texttt{DARA} : Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs (2024)

Abstract: 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 Decomposition-Alignment-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, 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.

## introduction language agents @xcite @xcite building on large language models (llms) have emerged as a new paradigm of artificial intelligence (ai) systems to perform complex tasks in various environments. these agents primarily rely on llms as central controllers to perceive environments, autonomously plan, and reason to fulfill user tasks. however, operating within environments rich in structured data, such as knowledge graphs (kgs) @xcite , presents unique challenges for the agents. for instance, performing question answering over knowledge graphs (kgqa) demands various capabilities from tool kits get_relations get_neighbours get_attributes