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KGLM-QA: A Novel Approach for knowledge Graph-Enhanced Large language Models for Question Answering 15

KGLM-QA: A Novel Approach for Knowledge Graph-Enhanced Large...

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15th international conference on Information and knowledge Technology, IKT 2024

作者： Safaei, Alireza Akhavan Saboori, Pegah Ramezani, Reza Nematbakhsh, Mohammadali Department of Software Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Large language models excel in various natural language processing tasks but often struggle with knowledge-intensive queries, particularly those involve rare entities or require precise factual information. This paper presents a novel framework that enhances capabilities of an LLM-based question answering system by incorporating structured knowledge from knowledge graphs. Our approach employs entity extraction, semantic similarity scoring, and adaptive graph exploration to efficiently navigate and extract relevant information from knowledge graphs. The core of the presented solution is a knowledge graph-enhanced language model process that iteratively refines subgraph exploration and answer generation, complemented by a fallback mechanism for robustness across diverse question types. Experiments on location-based questions from the Entity Questions dataset demonstrate significant improvements in the quality of responses. Using the Gemini 1.5 Flash model, our system achieved an accuracy increase from 36% to 71% for partially correct answers and from 22% to 69% for exactly correct answers, as evaluated by human assessors. This approach offers a promising direction for developing more reliable and accurate question answering systems, particularly for queries involving long-tail entities or specific factual knowledge. © 2024 ieee.

关键词： knowledge graph

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AI-enabled Regulatory Change Analysis of Legal Requirements 32

AI-enabled Regulatory Change Analysis of Legal Requirements

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32nd ieee international Requirements engineering conference (RE)

作者： Abualhaija, Sallam Ceci, Marcello Sannier, Nicolas Bianculli, Domenico Briand, Lionel C. Zetzsche, Dirk Bodellini, Marco Univ Luxembourg SnT Luxembourg Luxembourg Univ Limerick Lero SFI Ctr Software Res Limerick Ireland Univ Ottawa Sch EECS Ottawa ON Canada Univ Luxembourg FDEF Luxembourg Luxembourg

ISBN: (纸本)9798350395129;9798350395112

Statutory law is subject to change as legislation develops over time - new regulation can be introduced, while existing regulation can be amended, or repealed. From a requirements engineering (RE) perspective, such change must be dealt with to ensure the compliance of software systems at all times. Understanding the implications of regulatory change on compliance of software requirements requires navigating hundreds of legal provisions. Analyzing instances of regulatory change entirely manually is not only time-consuming, but also risky, since missing a change may result in non-compliant software which can in turn lead to hefty fines. In this paper, we propose MURCIA, an automated approach that leverages recent language models to assist human analysts in analyzing regulatory changes. To build MURCIA, we define a taxonomy that characterizes the regulatory changes at the textual level as well as the changes in the text's meaning and legal interpretation. We evaluate MURCIA on four regulations from the financial domain. Over our evaluation set, MURCIA can identify textual changes with F1 score of 90.5%, and it can provide, according to our taxonomy, the text meaning and legal interpretation with an F1 score of 90.8% and 83.7%, respectively.

关键词： Regulatory Change Prompt engineering natural language processing (NLP) Large language Models (LLMs) ChatGPT Regulatory Compliance

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Option-Differentiated Clue Augmentation for Commonsense Question Answering

Option-Differentiated Clue Augmentation for Commonsense Ques...

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international Joint conference on Neural Networks (IJCNN)

作者： Hao, Li Deng, Wenqing Wang, Zhe Wang, Kewen Zhuang, Zhiqiang Yang, Dongyu Tianjin Univ Tianjin Peoples R China Griffith Univ Brisbane Australia

ISBN: (纸本)9798350359329;9798350359312

In commonsense question answering (CSQA), pre-trained language models are often used to generate background knowledge that provides clues to improve the interpretability and performance of CSQA models. However, these methods are often ineffective for generating clues to distinguish similar options, and they also have limitations in using generative capabilities to solve CSQA tasks. In this paper, we propose a new model for CSQA that can differentiate the options for a question more effectively. This model leverages the generative capabilities of the pre-trained model to better understand the relationship between question and candidate options, creating clues that aid in effectively distinguishing choices for the question. Moreover, a mechanism is introduced to encourage the model to discern distinctions among options. Our proposed model demonstrates superior performance on four CSQA datasets, outperforming comparable models.

关键词： natural language processing Question Answering Pre-Trained language Model

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SemantiLog: Log-based Anomaly Detection with Semantic Similarity 24

SemantiLog: Log-based Anomaly Detection with Semantic Simila...

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39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Shavit, Yoh Shteingart, Hanan Razmadze, Kathy Zahavi, Eitan Mataev, Gary Binshtock, Zachi NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Logs produced by software applications are invaluable for spotting deviations from expected system behavior. However, automatically detecting anomalies from log data is challenging due to the volume, semi-structured nature, lack of standard formatting, and potential evolution of log records over time. In this work, we approach log-based anomaly detection as a semantic similarity problem. We generate pairwise similarity scores using a general-purpose pre-trained language model and further augment them with ground-truth binary labels. The generated similarity labels supervise an encoder trained for semantic similarity. At inference time, anomalies are detected based on the cosine similarity between the encoded query sequence and the average normal encoding. Our method outperforms contemporary techniques on multiple benchmarks without template extraction or a fixed vocabulary and achieves competitive performance even when provided with limited abnormal examples.

关键词： Vocabulary Semantics Neural networks Benchmark testing Software natural language processing Encoding Anomaly detection Standards Software engineering

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Extracting Domain Models from Textual Requirements in the Era of Large language Models

Extracting Domain Models from Textual Requirements in the Er...

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ACM/ieee international conference on Model Driven engineering languages and Systems (MODELS)

作者： Arulmohan, Sathurshan Meurs, Marie-Jean Mosser, Sebastien McMaster Univ CAS Hamilton ON Canada McSCert Hamilton ON Canada Univ Quebec Montreal CIRST Montreal PQ Canada

ISBN: (纸本)9798350324983

Requirements engineering is a critical part of the software lifecycle, describing what a given piece of software will do (functional) and how it will do it (non-functional). Requirements documents are often textual, and it is up to software engineers to extract the relevant domain models from the text, which is an error-prone and time-consuming task. Considering the recent attention gained by Large language Models (LLMs), we explored how they could support this task. This paper investigates how such models can be used to extract domain models from agile product backlogs and compare them to (i) a state-of-practice tool as well as (ii) a dedicated natural language processing (NLP) approach, on top of a reference dataset of 22 products and 1, 679 user stories. Based on these results, this paper is a first step towards using LLMs and/or tailored NLP to support automated requirements engineering thanks to model extraction using artificial intelligence.

关键词： Domain Modeling natural language processing Large language Models Concept Extraction User stories

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ParaFusion-Extended: Large Scale Paraphrase Dataset Integrating Lexico-Phrasal knowledge 30th

ParaFusion-Extended: Large Scale Paraphrase Dataset Integrat...

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30th international conference on Computational and Experimental engineering and Sciences

作者： Jayawardena, Lasal Yapa, Prasan Robert Gordon Univ Garthdee HouseGarthdee Rd Aberdeen AB10 7AQ Scotland Inst Informat Technol 57 Ramakrishna Rd Colombo 00600 Sri Lanka

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Paraphrasing, the art of rephrasing text while retaining its original meaning, lies at the core of natural language understanding and generation. With the rise of demand for more domain-specialized models, high-quality data is more valued than ever;this includes paraphrasing. ParaFusion-Extend (PFE) is a large-scale dataset driven by Large language Models incorporating lexical and phrasal knowledge. The dataset is curated to contain high-quality diverse paraphrase pairs and also separate knowledge bases that could be used for research work and data augmentation models. We show that PFE offers around at least a 30% increase in syntactic and lexical diversity compared to the original data sources that are commonly used. We demonstrate the effectiveness of PFE on several downstream tasks such as few-shot learning and training on sentence embeddings. We utilize a gold-standard evaluation scheme, which is further strengthened by human evaluation that shows the potential of PFE in advancing paraphrase generation.

关键词： paraphrase generation natural language processing natural language generation knowledge representation large language models data augmentation sentence embeddings few-shot learning

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Personalized Academic Communication Recommendation System Based on knowledge Graph 5

Personalized Academic Communication Recommendation System Ba...

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5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Liu, Jiajia Luan, Meilin Jiang, Ning School of Information Engineering Wuhan University of Technology Wuhan China Wuhan University of Technology International Office Wuhan China

ISBN: (纸本)9798350386943

Scholar retrieval resources are faced with the problems of information overload, low efficiency, and low matching degree. To address this issue, we developed a personalized academic resource recommendation system based on the knowledge graph. The system consists of a data layer, a policy layer, and an application layer, which include four main functional modules: resource storage module, knowledge description module, wisdom recommendation module, and user management module. The back-end of the system utilizes the Spring Boot, Spring MVC, and MyBatis frameworks, while the front-end communicates with the back end via HTTP requests. The system database primarily utilizes MySQL and Neo4j. The system integrates knowledge graph and natural language processing technology to recommend high-quality academic resources for scholars. © 2024 ieee.

关键词： knowledge graph

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A Novel natural language processing Based Model for Plagiarism Detection 6

A Novel Natural Language Processing Based Model for Plagiari...

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6th ieee international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Patil, Rahul Kadam, Vaibhavi Nakate, Roshan Kadam, Sanjay Pattade, Suraj Mitkari, Mihir Bharati Vidyapeeth College of Engineering Navi Mumbai India

ISBN: (纸本)9798350306613

In order to detect plagiarism in academic and professional contexts, this work presents an intelligent natural language processing (NLP) method. The program outperforms conventional exact match algorithms by utilizing sophisticated NLP techniques, robust preprocessing and semantic analysis. It employs AHP, examines linguistic complexity, and identifies paraphrasing. Inspired by previous research, the strategy examines multi-word phrases and how frequently they are used to gain a more in-depth understanding. We contrast reputable and suspicious studies, assigning varying weights to different areas. Our comprehensive analysis demonstrates that the program performs admirably across a range of topic areas, improving plagiarism detection and maintaining the integrity of academic papers. © 2024 ieee.

关键词： AHP natural language processing Plagiarism Detection Semantic Analysis

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Backdoor Attack Detecting and Removing Based on knowledge Distillation for natural language Translation Model 9

Backdoor Attack Detecting and Removing Based on Knowledge Di...

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9th international conference on Computer and Communications, ICCC 2023

作者： Chen, Mao Pang, Lihui Tan, Qingyi Tang, Yilong Liu, Yulang Zhang, Wenwei University of South China School of Electrical Engineering Hengyang China Shenzhen Technology University Sino-German College of Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9798350317251

The lack of interpretability in Deep Neural Networks makes them susceptible to backdoor attacks. The attacker mixes the poisoned data with triggers into a clean dataset, and uses it to train a backdoor model. This model maintains high accuracy on clean data while outputting the attacker's desired target for the poisoned data. Due to the serious threat of backdoor attacks on DNN, backdoor defense on DNN is particularly important. In our work, we apply the knowledge distillation method in the visual field to natural language processing. The method of knowledge distillation involves removing toxic data from the poisoned training dataset and restoring the accuracy of the distilled model. In a defensive scenario, one assumption is that the defender can collect clean data without labels. We evaluated the effectiveness of knowledge distillation on strategies through two application scenarios in natural language processing and multiple models. By distilling and fine-tuning to disable backdoors, we further improved the classification accuracy of the distilled models. The experimental results indicate that the method of knowledge distillation can also effectively defend against backdoor attacks in natural language processing. © 2023 ieee.

关键词： Distillation

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Dynamic Scoring Code Token Tree: A Novel Decoding Strategy for Generating High-Performance Code 24

Dynamic Scoring Code Token Tree: A Novel Decoding Strategy f...

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39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Qu, Muzi Liu, Jie Kang, Liangyi Wang, Shuai Ye, Dan Huang, Tao Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Syst Software Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Comp Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Nanjing 211135 Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Within the realms of scientific computing, large-scale data processing, and artificial intelligence-powered computation, disparities in performance, which originate from differing code implementations, directly influence the practicality of the code. Although existing works tried to utilize code knowledge to enhance the execution performance of codes generated by large language models, they neglect code evaluation outcomes which directly refer to the code execution details, resulting in inefficient computation. To address this issue, we propose DSCT-Decode, an innovative adaptive decoding strategy for large language models, that employs a data structure named 'Code Token Tree' (CTT), which guides token selection based on code evaluation outcomes. DSCT-Decode assesses generated code across three dimensions-correctness, performance, and similarity-and utilizes a dynamic penalty-based boundary intersection method to compute multi-objective scores, which are then used to adjust the scores of nodes in the CTT during backpropagation. By maintaining a balance between exploration, through token selection probabilities, and exploitation, through multi-objective scoring, DSCT-Decode effectively navigates the code space to swiftly identify high-performance code solutions. To substantiate our framework, we developed a new benchmark, big-DS-1000, which is an extension of DS-1000. This benchmark is the first of its kind to specifically evaluate code generation methods based on execution performance. Comparative evaluations with leading large language models, such as CodeLlama and GPT-4, show that our framework achieves an average performance enhancement of nearly 30%. Furthermore, 30% of the codes exhibited a performance improvement of more than 20%, underscoring the effectiveness and potential of our framework for practical applications.

关键词： Code Generation Large language Model Performance Optimization

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