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Lenna: language Enhanced Reasoning Detection Assistant

Lenna: Language Enhanced Reasoning Detection Assistant

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wei, Fei Zhang, Xinyu Zhang, Ailing Zhang, Bo Chu, Xiangxiang AMAP Meituan Inc. China Peking University China

ISBN: (纸本)9798350368741

With the fast-paced development of multimodal large language models (MLLMs), we can now converse with AI systems in natural languages to understand images. However, the reasoning power and world knowledge embedded in the large language models have been much less investigated and exploited for image perception tasks. In this paper, we propose Lenna, a language enhanced reasoning detection assistant, which utilizes the robust multimodal feature representation of MLLMs, while preserving location information for detection. This is achieved by incorporating an additional token in the MLLM vocabulary that is free of explicit semantic context but serves as a prompt for the detector to identify the corresponding position. To evaluate the reasoning capability of Lenna, we construct a ReasonDet dataset to measure its performance on reasoning-based detection. Remarkably, Lenna demonstrates outstanding performance on ReasonDet and comes with significantly low training costs. It also incurs minimal transferring overhead when extended to other tasks. © 2025 ieee.

关键词： detection large language model multimodal reasoning

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Towards Understanding Contracts Grammar: A Large language Model-based Extractive Question-Answering Approach 32

Towards Understanding Contracts Grammar: A Large Language Mo...

引用

32nd ieee international Requirements engineering conference (RE)

作者： Rejithkumar, Gokul Anish, Preethu Rose Ghaisas, Smita TCS Res Pune India

ISBN: (纸本)9798350395129;9798350395112

Software engineering (SE) contracts play a pivotal role in Information Technology Outsourcing (ITO) projects. The obligations in SE contracts are known to be a useful source for deriving software requirements, thereby contributing to the overall Software Development Life Cycle (SDLC). Making sense of contractual obligations is an important first step in successfully executing software projects. This includes building compliant systems, meeting delivery deadlines, avoiding heavy penalties, and steering clear of expensive litigations. In this work, we present an approach to capture the essence of a contractual clause by extracting its Contracts Grammar. Through an exploratory study, we first identify the constituents of Contracts Grammar. Subsequently, we experiment with multiple approaches for the automated extraction of these constituents, including extractive question-answering, token classification, text-to-text generation, prompting, and regular expressions. The question-answering based approach performed the best in terms of high average ROUGE-L score of 0.81, and faster inference times. The work presented in this paper is a part of the Contracts Governance System (CGS) and is in the process of deployment within a large IT vendor organization.

关键词： text extraction deep learning natural language processing large language models question-answering token classification text-to-text generation prompting empirical research

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Defect Correction Method for Software Requirements Text Using Large language Models

Defect Correction Method for Software Requirements Text Usin...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Zhang, Li Ouyang, Liubo Xu, Zhuoqun Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Text defect correction holds a pivotal role within the domain of text processing. In the face of the rapid advancement of natural language processing (NLP) technologies, significant challenges persist in achieving precision and efficiency, particularly within specialized domains such as software requirements. For instance, traditional methods require labor-intensive verification, and the generative Large language Models(LLMs) paradigm suffers from illusions and knowledge deficits. So for professional software requirement text and defect correction processing, we construct a Chinese requirement text defect corpus containing 215 Chinese requirement texts and 10,049 manually labeled sentences and propose a contextual prompt pattern based on LLMs, which mainly includes the following steps: (1) Requirements text defect detection classification using fine-tuned BERT. (2) Extracting primary key information from requirement text using fine-tuned UIE. (3) The above results are embedded as contextual information in extensible prompt templates, respectively, and defects are corrected by interacting with generative LLMs. The experimental results show that by utilizing prompt learning and in-context learning techniques for requirement text defect correction, we achieved an outstanding improvement (30.3%) in defect correction effectiveness.

关键词： prompt learning large language models software requirements

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Efficient Textual Similarity using Semantic MinHashing

Efficient Textual Similarity using Semantic MinHashing

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international conference on Big Data and Smart Computing (BigComp)

作者： Nawaz, Waqas Baig, Maryam Khan, Kifayat Ullah Islamic Univ Madinah Dept Informat Syst Madinah Saudi Arabia NUCES FAST Univ Dept Comp Sci Islamabad Pakistan Birmingham City Univ Birmingham City Business Sch Coll Accountancy Finance & Econ Birmingham England

ISBN: (纸本)9798350370027;9798350370034

Quantifying the likeness between words, sentences, paragraphs, and documents plays a crucial role in various applications of natural language processing (NLP). As Bert, Elmo, and Roberta exemplified, contemporary methodologies leverage neural networks to generate embeddings, necessitating substantial data and training time for cutting-edge performance. Alternatively, semantic similarity metrics are based on knowledge bases like WordNet, using approaches such as the shortest path between words. MinHashing, a nimble technique, quickly approximates Jaccard similarity scores for document pairs. In this study, we propose employing MinHashing to gauge semantic scores by enhancing original documents with information from semantic networks, incorporating relationships such as synonyms, antonyms, hyponyms, and hypernyms. This augmentation improves lexical similarity based on semantic insights. The MinHash algorithm calculates compact signatures for extended vectors, mitigating dimensionality concerns. The similarity of these signatures reflects the semantic score between the documents. Our method achieves approximately 64% accuracy in the MRPC and SICK data sets.

关键词： MinHashing Semantic similarity WordNet natural language processing (NLP) Jaccard similarity Algorithm

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Confidence-Based Thresholding with Rule-Based Model for Agricultural Entity Extraction

Confidence-Based Thresholding with Rule-Based Model for Agri...

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2024 international conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Nismi Mol, E.A. Santosh Kumar, M.B. School of Engineering Cochin University of Science and Technology Division of Information Technology Kerala India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

Entity extraction is a vital baseline task in natural language processing for many applications, including relation extraction, knowledge extraction and knowledge graph construction. Entity extraction in particular domains like agriculture is explored less and needs further study. Rule-based methods are still relevant even in this machine learning and deep learning era, as they provide the possibility to start with the raw textual data without any annotated dataset. This paper proposes a rule-based approach with confidence thresholding for extracting agricultural entities from text documents and classifying them into the corresponding categories of Crop, Disease, Pathogen, and Nutrient. The Confidence score for each entity is determined based on the pattern used to extract the entity, suffix of the entity and frequency of occurrence of the entity. The entities are filtered based on the confidence score, and those with a confidence score above a threshold value are taken. The results are analyzed with a sampling approach for different threshold values and obtain a precision of 75.16% for Crop, 96.83% for Disease, 98.21% for Pathogen, and 98.11% for Nutrient with a confidence threshold value of 0.7. © 2024 ieee.

关键词： Deep learning Pathogens Communication systems Crops knowledge graphs Nutrients natural language processing Agriculture

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Leveraging LLMs for knowledge engineering from Technical Manuals: A Case Study in the Medical Prosthesis Manufacturing Domain 27

Leveraging LLMs for Knowledge Engineering from Technical Man...

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27th international conference on Information Fusion (FUSION)

作者： Incitti, Francesca Salfinger, Andrea Snidaro, Lauro Challapalli, Sri Univ Udine Dept Math Comp Sci & Phys Udine Italy Lima Corp SpA Via Nazl 52 I-33038 Villanova UD Italy

ISBN: (纸本)9798350371420;9781737749769

Ontologies are nowadays widely used to organize information across specific domains, being effective due to their hierarchical structure and the ability to explicitly represent relationships between concepts. knowledge engineering, like compiling companies' vast bodies of knowledge into these structures, however, still represents a time-consuming, largely manually performed process, esp. with significant amounts of knowledge often only recorded within unstructured text documents. Since the recently introduced Large language Models (LLMs) excel on text summarization, this raises the question whether these could be exploited within dedicated knowledge fusion architectures to assist human knowledge engineers by automatically suggesting relevant classes, instances and relations extracted from textual corpora. We therefore propose a novel approach that leverages the taxonomic structure of a partially defined ontology to prompt LLMs for hierarchical knowledge organization. Unlike conventional methods that rely solely on static ontologies, our methodology dynamically generates prompts based on the ontology's existing class taxonomy, prompting the LLM to generate responses that extract supplementary information from unstructured documents. It thus introduces the concept of using ontologies as scaffolds for guiding LLMs, in order to realize a mutual interplay between structured ontological knowledge and the soft fusion capabilities of LLMs. We evaluate our proposed algorithm on a real-world case study, performing a knowledge fusion task on heterogeneous technical documentation from a medical prosthesis manufacturer.

关键词： Large language Models knowledge engineering Ontology Population Soft Fusion natural language processing

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Ophthalmic Disease Zero-Shot Question Answering Through knowledge Triple Augmented language Model Prompting 7

Ophthalmic Disease Zero-Shot Question Answering Through Know...

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7th international conference on Machine Learning and natural language processing, MLNLP 2024

作者： Wang, Zihan Huo, Hong Xu, Renxin Yang, Shijie Fang, Tao Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350354973

Ophthalmic disease zero-shot question answering aims to answer questions about ophthalmic disease in natural language without any model training. Although the pretrained large language models (LLMs) have stored quantities of general knowledge of the real-world and made zero-shot question answering come true, they ususally have insufficient ophthalmic knowledge, and fine-tuning LLMs to update their internal ophthalmic knowledge is very compute-intensive and expensive. By utilizing the specialized knowledge in the self-built ophthalmic disease knowledge graph (ODKG) to augment LLMs, the proposed knowledge Triple Augmented language model Prompting (KTAP) has well implemented ophthalmic disease zero-shot question answering. Our KTAP is primarily divided into three modules: entity linking, knowledge retriever and knowledge injection. The entity linking module extracts ophthalmic disease entities from the question by a prompt-based zero-shot entity linking method, and a subgraph related to these ophthalmic disease entities is obtained from ODKG. The knowledge retriever module consists of two submodules, the text embedding and the similarity matching. The former is responsible for calculating embeddings of both each triple in the subgraph and entities in the question, while the latter calculates the semantic similarity between each triple embedding and the question embedding to obtain the top K triples most relevant to the question. In the knowledge injection module, a prompt template based on the top K triples and the question is designed to guide LLMs to perform ophthalmic disease zero-shot question answering. The experiments have shown that in the zero-shot question answering task of ophthalmic disease, the proposed KTAP has outperformed other language model prompting baselines by an average of 16% in the precision of generated answers. © 2024 ieee.

关键词： knowledge graph

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The Application of Fine-Tuning on Pretrained language Model in Information Extraction for Fault knowledge Graphs 9

The Application of Fine-Tuning on Pretrained Language Model ...

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9th international conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Kaiwen Su, Feiyu Huang, Yixiang Li, Yanming Wu, Fengqi Mao, Yuhan School of Mechanical and Power Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University The State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai200333 China

ISBN: (纸本)9798350376548

Constructing fault knowledge graphs holds significant importance for achieving intelligent maintenance and diagnosis in high-end equipment manufacturing. Effective information extraction and knowledge graph construction have proven challenging due to the lack of standardized representation of semantically complex unstructured text in the industrial domain. Therefore, in this study, we performed fine-tuning on the pre-trained language model (ChatGLM2-6B) with specific prompts to achieve information extraction from fault-related texts, ultimately leading to the construction of a fault knowledge graph. Experimental results demonstrate that the proposed method not only supports fine-tuning with limited data but also exhibits enhanced capability in understanding complex semantics related to fault symptoms and causes. © 2024 ieee.

关键词： knowledge graph

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Hybrid Model for Predicting Used Car Prices: Integrating natural language processing with Random Forest Regressor 2

Hybrid Model for Predicting Used Car Prices: Integrating Nat...

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2nd ieee international conference on Advances in Information Technology, ICAIT 2024

作者： Tyagi, Shiva Sirohi, Shagun Singh, Yash Siddhant Vishwakarma, Akash Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350383867

The automotive sector has a notable upswing in the sales of pre-owned vehicles, hence necessitating the development of precise pricing models to enable knowledgeable decision-making for both purchasers and vendors. In order to predict used automobile prices, we provide a novel hybrid methodology in this work that combines natural language processing (NLP) methods with the Random Forest regressor. Our method leverages structured and unstructured data sources to extract valuable contextual information that is hidden in textual listings for automobiles. Preprocessing a heterogeneous dataset with unstructured textual descriptions and structured attributes (make, model, year, mileage) is part of the methodology. We leverage natural language processing (NLP) approaches to extract informative features from textual representations, improving the model's comprehension of subtleties in the conditions and attributes linked to individual car listings. ©2024 ieee.

关键词： Random forests

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KOSA: KO Enhanced Salary Analytics based on knowledge Graph and LLM Capabilities 23

KOSA: KO Enhanced Salary Analytics based on Knowledge Graph ...

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23rd ieee international conference on Data Mining (ieee ICDM)

作者： Huang, Fei Deng, Yi Zhang, Chen Guo, Menghao Zhan, Kai Sun, Shanxin Jiang, Jinling Sun, Zeyi Wu, Xindong Zhejiang Lab Res Ctr Knowledge Engn Hangzhou 311121 Zhejiang Peoples R China

ISBN: (纸本)9798350381641

knowledge base question answering (KBQA) is designed to respond to natural language inquiries by utilizing factual information, such as entities, relationships, and attributes, derived from a knowledge base (KB). The advent of large language models (LLMs) has significantly boosted the performance of KBQA, owing to their exceptional capabilities in content comprehension and generation. In this paper, we present a knowledge Ocean enhanced Salary Analytics (KOSA) system based on knowledge graphs and LLMs tailored to employee salary data from a public university. This system encompasses an interactive conversational interface, visualization of knowledge graphs, and advanced data analysis. By employing the framework of knowledge engineering, we enable knowledge graph modeling, Cypher (the query engine of Neo4j) reasoning, and question answering functionalities. Furthermore, machine learning algorithms are integrated to facilitate advanced features, such as salary prediction and allocation.

关键词： KBQA LLM Salary distribution knowledge engineering.

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