Aug 2026· Machine Learning and Knowledge Extraction· Vol 8, pp. 261· 0 citations· 45 references
TL;DR
MGDR-SDR is proposed, a training-free approach that applies constituency parsing to derive three complementary representations of the utterance that drive a multi-granular retrieval procedure that aggregates evidence across dense and sparse channels, yielding broader and more precise demonstration coverage.
Abstract
Translating natural language into graph query languages (NL2GQL) enables non-expert users to access graph databases, but supervised parsers depend on large annotated corpora and costly retraining whenever the schema evolves. Few-shot in-context learning offers a training-free alternative, yet its effectiveness hinges on demonstration selection, and conventional retrieval based on sentence-level matching and a single similarity signal often overlooks beneficial exemplars. We propose MGDR-SDR, a training-free approach that applies constituency parsing to derive three complementary representations of the utterance: a shallow abstraction preserving fine-grained lexical and syntactic detail, a deep abstraction exposing the intent-level syntactic skeleton, and a noun phrase list capturing concrete graph elements. These representations drive a multi-granular retrieval procedure that aggregates evidence across dense and sparse channels, yielding broader and more precise demonstration coverage. We evaluate MGDR-SDR on SPARQL generation over KQA Pro and Cypher generation over ZOGRASCOPE. Without any training, it reaches 86.58% Hits@1 on KQA Pro, surpassing strong supervised parsers and setting a new state-of-the-art among few-shot methods, and it transfers across GQL formalisms, attaining the best iid accuracy and a balanced generalization profile that surpasses all open-source few-shot baselines on ZOGRASCOPE. These results show that syntax-derived, multi-granular retrieval is an effective and generalizable strategy for few-shot NL2GQL semantic parsing.
Dependency Parsing forms one of the key building blocks of natural language processing tasks such as semantic role labelling, machine translation, and information extraction since it furnishes the syntactic structure on which those high-level tasks depend. While transformer-based architectures can model highly expressive representations, there exists a tough trade-off between the two existing methods: specialized parsers can reach high benchmark scores but need special parsing heads and specific training processes, while generative large language models have flexible architecture but do not have any structural guarantee of forming dependency trees as outputs. This paper introduces SARG-QLoRA, a method which solves this trade-off problem by merging Syntax-Aware Retrieval Guidance (SARG) with Quantized Low-Rank Adaptation (QLoRA), allowing for the generation of dependency structures on a consumer-grade hardware setup. In contrast to conventional retrieval-augmented generation approaches where context selection is performed based on semantic similarity, the proposed approach generates a FAISS index based on multilingual-e5-base embedding of training examples and performs syntactically similar retrieval of context. In addition to a Tree Validity Checker (TVC) that validates four well-formedness formal criteria after generation, a structural validity dimension is introduced that is orthogonal to traditional attachment scores metrics. After training solely on the Universal Dependencies English Web Treebank and fine-tuning just 0.75% of the parameters (24.3 million out of 3.24 billion), SARG-QLoRA reaches 81.35% Unlabelled Attachment Score (UAS) and 78.28% Labelled Attachment Score (LAS) — just 1.07 UAS away from the specialized Stanza parser — while preserving zero-shot transfer capability to Spanish (62.81% UAS), German (61.51%), and French (59.11%). TVC analysis shows structural validity for 87.14% of the test set, balanced brackets for 97.35%, and one root per tree for 99.95%.
Nagarchi Arshad· Journal of Intelligent Decis...· 0 citations
Large Language Models (LLMs) offer strong capabilities for Natural Language Processing, yet their inherent uncertainty often produces hallucinations, confident but incorrect statements, which is critical in domains requiring precise knowledge representation. Retrieval-Augmented Generation (RAG) reduces this risk through information retrieval, but standard pipelines still suffer from fragmented context and weak alignment between queries and legal provisions, limiting trustworthy knowledge extraction. This study proposes a Metadata-Aware RAG architecture to improve grounding in large legal corpora. It integrates: 1) Sub-chunking with Legal Metadata Inheritance, which transforms unstructured legal PDFs into granular, metadata-rich fragments; and 2) an Adaptive Filter Creator, a pipeline that extracts structured constraints and compiles optimized hybrid retrieval queries. These components enhance semantic alignment, reduce uncertainty-driven hallucinations, and strengthen neural information retrieval. Using a curated Peruvian labor law corpus and 150 manually validated question–answer pairs, the system was evaluated across three LLMs (Llama-3.1-8B, GPT-OSS-20B, Gemma-3-27B). The proposed architecture achieves double-digit improvements over a Naive RAG baseline across all four RAGAS metrics—Context Precision, Context Recall, Factual Correctness, and Faithfulness—with gains ranging from 13.10% to 28.15%; notably, Faithfulness surpasses 0.90 for Gemma-3-27B. Statistical analysis confirms significance (t(11) = 15.49, p = 4.06 × 10−9) with an extremely large effect size (Cohen’s d = 4.47). Regression results show minimal influence of model size (slope < 0.005), indicating that retrieval design has a stronger influence than parameter count in the evaluated setting.
Alexandra V. Jove-Ticona, Luis J. Duarte-Coaquera, Israel N. Chaparro-Cruz et al.· International Journal of Adv...· 0 citations
This study proposes STaR, a novel retriever fine-tuning framework that integrates BM25 similarity graph-based soft labeling with a triplet similarity learning strategy based on Sentence-BERT (SBERT), and introduces a triplet-aware SBERT training architecture that explicitly models relative semantic distances between queries and candidate passages, significantly enhancing retrieval ranking precision and semantic robustness.
Jiali Jiang, Chih-Yung Chang, Youxi Li et al.· Multimedia Systems· 0 citations
General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We introduce AVA, a systematic framework for evaluating whether embeddings distinguish logic-sensitive relational semantics in ontologies and knowledge graphs. AVA comprises 171,007 contrastive triplets derived from 163 heterogeneous ontologies using hierarchy inversion, relation substitution, and disjointness injection. Each triplet contains an ontology statement, a semantically equivalent paraphrase, and a logic-sensitive hard negative with contradictory relational meaning. We evaluate more than 25 state-of-the-art embedding models and find substantial limitations: the best model achieves only 0.739 triplet accuracy, while hard negative accuracy falls to 0.135. Fine-tuning improves discrimination by a large margin but transfers poorly to downstream Semantic Web tasks, including taxonomy discovery and ontology alignment. Further analysis suggests that improvements stem partly from perturbation-specific pattern recognition rather than robust ontological understanding. These findings reveal a persistent gap between linguistic representation learning and ontology-level discrimination, challenging the assumption that strong NLP benchmark performance translates to Semantic Web competence.
Hamed Babaei Giglou, Jennifer D’Souza, S. Auer· 0 citations
Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated independently for each candidate triple and may violate fundamental relational constraints such as transitivity, symmetry, and functional uniqueness, leading to contradictory and unreliable outputs. We propose CONSISTRE, a unified consistency-aware framework for DocRE that addresses this limitation through two complementary tracks. The first operates at inference time for black-box LLMs, combining constraint-aware prompting, constraint-based verification, and iterative self-reflection to refine predictions without task-specific fine-tuning. The second injects consistency knowledge into smaller open-source models via a knowledge distillation and reinforcement learning pipeline: reasoning traces from a powerful teacher are distilled into a student via supervised fine-tuning, followed by GRPO alignment using a composite reward that jointly optimizes extraction performance and relational consistency. Together, the two tracks cover both API-accessible and locally deployable scenarios under a unified consistency formulation. Experiments on DocRED show that both tracks outperform their baselines, with the inference-time track achieving competitive F1 using off-the-shelf black-box LLMs and the training-time track substantially narrowing the gap between 7--8B open-source models and state-of-the-art proprietary LLMs at a fraction of their inference cost. Ablation studies confirm that explicit consistency modeling mitigates relational contradictions and enhances the reliability of LLM-based DocRE across both deployment paradigms.
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.
John Bosco Mugeni, Steven J. Lynden, Toshiyuki Amagasa et al.· International Journal of Dat...· 0 citations
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