Knowledge base question answering (KBQA) is recognized as a challenging task, especially when parsing complex questions into executable logical forms. Traditional semantic parsing (SP)-based approaches exhibit inconsistent performance in handling various complex questions. As large language models (LLMs) have exhibited exceptional reasoning ability and language comprehension, recent studies have employed LLMs for semantic parsing to directly generate logical forms that can be executed on knowledge bases (KBs) to achieve the desired results. However, these methods of relying exclusively on LLMs to ensure grammaticality, faithfulness, and controllability may diminish their effectiveness due to hallucinations in the reasoning process. In this paper, we introduce Rule-KBQA, a framework that employs learned rules to guide the generation of logical forms. The proposed method contains two phases, an induction phase and a deduction phase. In the induction phase, we initially extract rules from the existing data and then employ the Rule-Following Fine-Tuned (RFFT) LLM to generate additional rules, ultimately constructing a comprehensive rule library. In the deduction phase, a symbolic agent, guided by learned rules, explores the environment KB to incrementally construct executable logical forms. Meanwhile, we leverage the discriminative capability of LLMs to evaluate the plausibility of candidate decisions. Extensive experiments indicate that our method achieves competitive results on standard KBQA datasets, clearly demonstrating its effectiveness.

Knowledge Graph Question Answering (KGQA) aims to automatically answer natural language questions by reasoning across multiple triples in knowledge graphs (KGs). Reinforcement learning (RL)-based methods are introduced to enhance model interpretability. Nevertheless, when addressing complex questions requiring long-term reasoning, the RL agent is usually misled by aimless exploration, as it lacks common learning practices with prior knowledge. Recently, large language models (LLMs) have been proven to encode vast amounts of knowledge about the world and possess remarkable reasoning capabilities. However, they often encounter challenges with hallucination issues, failing to address complex questions that demand deep and deliberate reasoning. In this paper, we propose a collaborative reasoning framework (CRF) powered by RL and LLMs to answer complex questions based on the knowledge graph. Our approach leverages the common sense priors contained in LLMs while utilizing RL to provide learning from the environment, resulting in a hierarchical agent that uses LLMs to solve the complex KGQA task. By combining LLMs and the RL policy, the high-level agent accurately identifies constraints encountered during reasoning, while the low-level agent conducts efficient path reasoning by selecting the most promising relations in KG. Extensive experiments conducted on four benchmark datasets clearly demonstrate the effectiveness of the proposed model, which surpasses state-of-the-art approaches.

This study explores the realm of knowledge base question answering (KBQA). KBQA is considered a challenging task, particularly in parsing intricate questions into executable logical forms. Traditional semantic parsing (SP)-based methods require extensive data annotations, which result in significant costs. Recently, the advent of few-shot in-context learning, powered by large language models (LLMs), has showcased promising capabilities. Yet, fully leveraging LLMs to parse questions into logical forms in low-resource scenarios poses a substantial challenge. To tackle these hurdles, we introduce Interactive-KBQA, a framework designed to generate logical forms through direct interaction with knowledge bases (KBs). Within this framework, we have developed three generic APIs for KB interaction. For each category of complex question, we devised exemplars to guide LLMs through the reasoning processes. Our method achieves competitive results on the WebQuestionsSP, ComplexWebQuestions, KQA Pro, and MetaQA datasets with a minimal number of examples (shots). Importantly, our approach supports manual intervention, allowing for the iterative refinement of LLM outputs. By annotating a dataset with step-wise reasoning processes, we showcase our model’s adaptability and highlight its potential for contributing significant enhancements to the field.

This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC’s overall superiority and effectiveness of each component.