Knowledge Base Question Answering (KBQA) aims to answer natural language questions by reasoning over large-scale structured Knowledge Bases (KBs). Among existing approaches, semantic parsing-based methods have emerged as a mainstream solution, where Large Language Models (LLMs) are employed to translate questions into structured graph queries such as Logical Forms (LFs). However, this paradigm faces two critical challenges: (1) The complex semantic mapping and graph retrieval operations render direct one-shot LF generation difficult; (2) LLMs suffer from inherent hallucination issues, generating semantically plausible-seeming but factually incorrect or invalid LFs, which are non-executable. To address these challenges, this article proposes HP-Corr , a novel framework that integrates H op-wise P lanning with iterative explainable self- Corr ection for faithful knowledge reasoning. Specifically, the system utilizes a fine-tuned open source LLM for query planning and explainable self-correction. The query planner generates reasoning paths hop-by-hop, while an explainable self-correction provides hop-wise feedback, enabling interpretable path editing based on existing reasoning paths and retrieved KB knowledge. By introducing the dual-module cooperative architecture, our system performs iterative plan-then-correct to refine query paths progressively, ensuring answer reliability and LFs executability. Experimental results demonstrate significant improvements, with our approach achieving higher accuracy while substantially reducing the search space, particularly in complex multi-hop KBQA scenarios.

The large-scale knowledge base question-answering (KBQA) has become increasingly vital across various fields. In the era of large language models (LLMs), leveraging knowledge base retrieval combined with large models for knowledge reasoning has become the mainstream approach for KBQA. However, this method faces two primary challenges: (1) the high computational cost and low accuracy of similarity-based path retrieval, and (2) the relatively low accuracy of directly obtaining answers from large models. In this paper, we introduce a novel method of retrieval enhancement-stepwise reasoning (RESR), which transforms path retrieval into text semantic understanding to minimize unnecessary interference from path information in the reasoning process, guiding the LLM to generate interpretable reasoning paths rather than directly producing answers. Specifically, RESR fine-tunes the generative model through text semantic understanding to swiftly and accurately filter path information relevant to the query from a large-scale knowledge graph (KG). Additionally, we employ the Chain-of-Thought (CoT) method to guide LLMs in step-by-step reasoning, verifying the logical coherence of reasoning paths rather than directly deriving answers. Our proposed method achieves state-of-the-art (SOTA) performance on the WebQuestionsSP (WQSP) and ComplexWebQuestions (CWQ) benchmarks.

Fine-tuning pre-trained language models (PLMs) for downstream tasks has achieved remarkable success in various natural language processing (NLP) applications. To further enhance the overall performance of PLMs across different NLP tasks, we propose a novel adversarial contrastive training (ACT) method that incorporates adversarial perturbations in the parameter space. Specifically, ACT introduces adversarial perturbations to the model’s parameters, deliberately degrading its performance on downstream tasks. Subsequently, we apply contrastive learning to align the representations of specific tasks with those of the perturbed model, thereby improving the model’s robustness and generalization ability. Experimental results demonstrate that ACT significantly enhances the generalization performance of PLMs and achieves state-of-the-art results on several text classification benchmarks.

There are a large number of text processing tasks in web applications, such as sentiment classification, summary extraction, and question answering. Recently, fine-tuning pre-trained language models (PLMs) to adapt to downstream text-processing tasks has attracted much attention. However, due to the differences in data, model, and tasks between the pre-training and fine-tuning processes, the fine-tuning process may suffer from catastrophic forgetting of pre-training knowledge, which may implicitly limit the model's performance and generalization ability. To address these challenges, we propose a novel dual-model framework, termed as consistency alignment (CoAi). The insight of CoAi lies in building an auxiliary model that simulates the distribution of pre-training knowledge in real-time according to the current task, and co-training the task-specific model and the auxiliary model to balance the pre-training knowledge and task-specific knowledge during fine-tuning. Specifically, the auxiliary model is constructed on-the-fly to maintain the pre-training knowledge. Subsequently, CoAi simulates the pre-training process by performing distributional exploration in the parameter space, which is built upon our novel insight into the transformation between data and model parameter space. However, the objectives leveraged to construct the auxiliary model lead to the misalignment between the pre-training and task-specific knowledge. To alleviate the inconsistency, we employ an auxiliary variable to align the prediction distribution of the task-specific and the auxiliary models, inspired by constrastive clustering. We validate the effectiveness of CoAi on nine classic classification tasks and three generation tasks, showing consistent and significant improvements compared with state-of-the-art methods.