Design and Evaluation of Large Language Model Approaches for Text2Cypher Generation in Domain-Specific Knowledge Graphs

GCA-KBQA: A Step-Wise Logical Form Generation Approach for KBQA with Knowledge-Assisted Calibration

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.

Augmenting Datasets for Fine-Tuning Large Language Models Using Semantic Variations

This study explores a semantic variation methodology to augment training data by generating question-answer pairs with explicit control over semantic similarity, and shows that semantically controlled augmentation improves domain-specific knowledge acquisition while preserving consistency.

Optimizing sample selection for large language model-based entity matching using AssistEM

AssistEM, a framework for efficient LLM adaptation to EM via principled data selection, demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models.

PYTHIA: Universal Question Answering over Knowledge Graphs

Knowledge graph question answering (KGQA) focuses on answering questions with data retrieved from knowledge graphs (KGs). Although Large Language Models (LLMs) have enabled the development of innovative KGQA systems, serious challenges remain. Approaches that utilize supervised finetuning are accurate and responsive, but also require large training datasets that provide good coverage of both the target knowledge graph and expected user questions. Likewise, integrating KG-specific optimizations improves accuracy and responsiveness but limits versatility and is susceptible to updates to the KG's ontology. LLMs have facilitated the development of training-free and KG-agnostic solutions that utilize reasoning and real-time decision making to explore the target KG, but the semi-structured nature of KGs makes this a complex and unpredictable task, leading to performance degradation and longer response times. In this paper we present PYTHIA, a training-free, plug-and-play solution for KGQA over any RDF KG. At its core, PYTHIA consists of an LLM agent guided by a relation-centric conceptual model of the KGQA task which is acted upon through a suite of tools for entity linking, graph exploration and query execution. The agent is responsible for interpreting user intent, orchestrating tool calls, and evaluating tool output, while the tools themselves present simple interfaces that abstract complex processes, reducing cognitive load on the agent. To assess its viability, we perform a comprehensive evaluation of PYTHIA and prior systems across four knowledge graphs and six datasets. The results show PYTHIA achieving a median performance improvement of 15.31% while improving cost and time efficiency at the performance frontier.

Efficiency Analysis of Fine-Tuning Compact Language Models for Biomedical Information Retrieval Systems

Combining feedback enhancement with knowledge graphs in large language models

Experiments on three different domain tasks show that FKGLM can effectively integrate LLMs and large-scale knowledge graphs, leading to a significant enhancement in the reasoning capabilities of LLMs.