Jianqi Gao, Hang Yu, Jian Cao, Ranran Bu, Jinghua Tang, Nengjun Zhu, Yonggang Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Knowledge base question answering (KBQA) aims to answer natural language questions using large-scale knowledge bases (KBs). Among various KBQA approaches, semantic parsing-based (SP-based) methods have demonstrated strong effectiveness by generating concise logical forms (LFs) that capture complex subgraph structures and semantic information. Recent research suggests that integrating large language models (LLMs) with SP can achieve significant improvements in the performance and efficiency of KBQA by facilitating the direct generation of LFs with minimal retrieval. However, generating complete LFs with LLMs continues to pose a challenge due to the complexity of the required graph structures and constraints, leading to the significant issue of non-executability. To address these challenges, we propose GCA-KBQA, a step-wise fine-tuned LLM-based framework that employs hop-wise generation, knowledge-assisted calibration, and path-level assembly to construct complete LFs for KBQA. Specifically, we decompose the complex SP process into manageable steps: first, we iteratively generate LFs for each topic entity one hop at a time using a fine-tuned LLM, leveraging KB knowledge to calibrate intermediate outputs and mitigate error propagation. Subsequently, we guide the LLM in assembling path-level LFs from different topic entities, resulting in optimized final LF. We evaluate the proposed method on four KBQA benchmarks spanning two distinct KBs, demonstrating its superior performance compared to state-of-the-art baselines. The code is available at https://github.com/pvfeldt/GCA-KBQA.

Knowledge base question answering (KBQA) refers to the task of answering natural language questions using factual information from large-scale knowledge bases (KBs). To obtain accurate answers, recent research optimizes semantic parsing methods, a major KBQA approach, with large language models (LLMs), where concise logical forms (LFs) are generated by LLMs and executed in KBs. Although these methods demonstrate superior performance, they still encounter the problem that some generated LFs fail to yield answers when executed, significantly limiting their effectiveness. To mitigate this issue, we propose KARV, a Knowledge-Assisted reasoning path Reconstruction and hierarchical Voting approach for non-executable LFs. This method extracts semantic knowledge from KBs as guidance to correct and reconstruct reasoning paths, deriving answers through a voting-based strategy. The insight is that non-executable LFs generated by LLMs still contain rich semantic information, and the knowledge retrieved from KBs can effectively correct them. Specifically, we fine-tune LLMs to generate high-quality LFs, and the nonexecutable LFs are decomposed into multiple path branches based on mentioned entities. Semantic knowledge from KBs is then leveraged to correct the entities and relations within these branches, effectively reconstructing the reasoning paths. To obtain precise final answers, we apply a hierarchical voting strategy both within and across the non-executable LFs. Our proposed method achieves state-of-the-art performance on benchmarks including WebQuestionSP (WebQSP), ComplexWebQuestions (CWQ), and FreebaseQA.

In recent years, leveraging pre-trained language models (PLMs) to generate embeddings for downstream tasks has achieved remarkable success. To further enhance the adaptability of PLMs to downstream tasks, the prevailing strategy involves incorporating auxiliary tasks as regularization terms, (e.g., contrastive learning), for fine-tuning the pre-trained models. However, this approach encounters challenges due to task conflicts between auxiliary tasks and specific downstream tasks. To overcome these issues, we introduce a novel strategy termed Consistency Adversarial Training (CAT). CAT first dynamically identifies the most inconsistent cases between specific and auxiliary tasks by introducing perturbations and then eliminates the inconsistency in an adversarial learning manner. Performance evaluations on the GLUE benchmark demonstrate that CAT minimizes task conflicts during the fine-tuning process of PLMs, leading to a notable improvement in the overall fine-tuning performance of PLMs.

The 3D reconstruction of the electron scattering intensity of a virus from cryo electron microscopy is essentially a 3D tomography problem in which the orientation of the 2D projections is unknown. Many biological problems concern mixtures of different types of virus particles or mixtures of different maturation states of the same type of virus particle. For a variety of reasons, especially low SNR, it can be very challenging to label the type or state shown in an individual image. Algorithms capable of computing multiple reconstructions, one for each type or state, based on images which are not labeled according to type or state, are described and demonstrated on experimental images.