A large language model-based question-answering system for crack information

An Intelligent PDF Question-Answering System; A Retrieval-Augmented Generation Approach

Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.

Domain-Specific Retrieval-Augmented Generation for Metallurgical R&D Knowledge Bases: A Hybrid Graph-Enhanced Approach

Metallurgical R&D search is difficult for a practical reason: useful evidence is rarely defined by one keyword. A production-support question can depend at the same time on material grade, process route, defect mechanism, property, test method, and numerical conditions. Conventional retrieval-augmented generation (RAG) pipelines largely treat document chunks as independent text and can therefore miss relations that matter for process monitoring, fault diagnosis, and engineering decision support. We evaluate a confidence-adaptive graph-enhanced retrieval layer for metallurgical RAG in a controlled synthetic benchmark with explicitly specified generation and evaluation rules. The benchmark contains 300 generated heterogeneous records derived from a seven-block source distribution and 30 material–process–defect–property archetypes, together with 180 frozen queries: 60 exact, 60 paraphrased, and 60 multi-hop. The main run evaluates robustness to incomplete structured metadata, with 10% missing and 4% erroneous categorical fields. Entity and relation extraction from raw documents is outside the evaluated scope. We compare BM25, TF-IDF, latent semantic analysis, a sparse + dense hybrid, graph-only retrieval, two ablations, and the proposed adaptive hybrid. On the complete query set, the proposed method obtains MRR = 0.992, Precision@5 = 0.980, Recall@10 = 0.859, and nDCG@10 = 0.948. Relative to the sparse + dense hybrid, nDCG@10 increases by 0.186 (24.4%); the paired 95% bootstrap interval is in the range of 0.166–0.206, and the Holm-adjusted Wilcoxon p-value is 2.59 × 10−29. Under severe degradation with 40% missing and 16% erroneous metadata, the adaptive method retains mean nDCG@10 = 0.791, compared with 0.650 for graph-only retrieval and 0.762 for the metadata-independent sparse + dense hybrid. A 5000-run Monte Carlo analysis estimates 6763 chunks and 58.70 MB for indexed vectors plus metadata at a 512-token chunk size and 64-token overlap. The results show how the retrieval rule behaves under controlled conditions; they are not evidence of plant-level effectiveness or of the quality of generated answers. Those questions require external, expert-labeled validation.

A Named Entity Recognition Method for GIS Defect Texts Incorporating an Engineering Format-Aware Masking Strategy

Named entity recognition (NER) is a key technique for extracting entities such as equipment, components and defect types from GIS defect texts, providing a basis for subsequent knowledge graph construction. However, GIS defect texts contain many engineering structures, including engineering abbreviations, equipment numbers and phase identifiers, making it difficult for general-purpose models to stably recognize their semantic associations and entity boundaries. To address this problem, this paper proposes an engineering format-aware masking strategy. The strategy identifies candidate fragments using format rules for phase identifiers, measurement value-unit patterns and engineering abbreviations and preferentially selects them as perturbation targets to strengthen the model’s understanding of engineering structures and their contextual relationships. Bidirectional long short-term memory is used to extract bidirectional sequence features, and a conditional random field is used to model transition constraints between labels and obtain the globally optimal label sequence. The results show that the proposed model achieves precision, recall and F1 scores of 0.89, 0.92 and 0.90, respectively. The analysis indicates that the proposed method improves entity recognition for phase-related structures, engineering abbreviations and equipment hierarchy fragments.

Autonomously Detecting Defects in Circular Production Using Multimodal Large Language Models and Retrieval Augmentation

CoAL-RAG: A Complexity-Aware Legal Retrieval-Augmented Generation Method

Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requirements for both answer quality and efficiency in high-risk scenarios. To address this issue, this paper proposes CoAL-RAG, a complexity-aware legal retrieval-augmented generation method, which constructs a multi-dimensional evaluation mechanism based on ``question essence''and ``retrieval consistency''to enable adaptive routing of retrieval strategies. First, the reasoning demand is quantified according to the logical structure of the question. Then, the discrepancy between semantic retrieval and keyword retrieval is utilized to indirectly reflect problem complexity, thereby selecting the most appropriate retrieval strategy and dynamically filtering contextual information. Experimental results demonstrate that the proposed method significantly outperforms baseline models not only on Chinese legal benchmarks (SocialLawQA, LawBench) but also demonstrates strong cross-jurisdictional generalization on English datasets (LexGLUE, CaseHold). Specifically, on Chinese datasets, the BLEU score improves by 42.5\% and ROUGE-L reaches 3.6 times that of knowledge graph-based methods. On English benchmarks, CoAL-RAG maintains highly competitive accuracy, achieving an optimal balance between generation quality, deep logical reasoning, and system efficiency across different legal systems.

General-purpose named entity recognition using transformer-based fine-tuned approach

A transformer-based fully fine-tuned XLNet model with 117M parameters is proposed, designed to recognize and extract named entities form general domain data and can be used for content recommendation, information retrieval in Q&A systems.