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GraphCoder: Enhancing Repository-Level Code Completion via Coarse-to-fine Retrieval Based on Code Context Graph 24

GraphCoder: Enhancing Repository-Level Code Completion via C...

引用

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

作者： Liu, Wei Yu, Ailun Zan, Daoguang Shen, Bo Zhang, Wei Zhao, Haiyan Jin, Zhi Wang, Qianxiang PKU MoE Key Lab High Confidence Software Technol PKU China Sch Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Huawei Cloud Comp Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches;based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.

关键词： Code completion Large language model Retrieval augmented generation Code graphs

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Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study

Abstractions, Scenarios, and Prompt Definitions for Process ...

引用

21st international conference on Business Process Management (BPM)

作者： Berti, Alessandro Schuster, Daniel van der Aalst, Andwil M. P. Rhein Westfal TH Aachen Proc & Data Sci Chair Aachen Germany Fraunhofer FIT St Augustin Germany

ISBN: (纸本)9783031509735;9783031509742

Large language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparable to humans for many proficient tasks. The analysis of business processes could benefit from a natural process querying language and using the domain knowledge on which LLMs have been trained. However, it is impossible to provide a complete database or event log as an input prompt due to size constraints. In this paper, we apply LLMs in the context of process mining by i) abstracting the information of standard process mining artifacts and ii) describing the prompting strategies. We implement the proposed abstraction techniques into pm4py, an open-source process mining library. We present a case study using available event logs. Starting from different abstractions and analysis questions, we formulate prompts and evaluate the quality of the answers.

关键词： Process Querying Prompting engineering Large language Models ChatGPT

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Adaptive Online Design knowledge Push Method Based on Context Navigation 2

Adaptive Online Design Knowledge Push Method Based on Contex...

引用

2nd international conference on Design Science, ICDS 2024

作者： Wei, Wei Liu, Meng-Hui Huang, Yu-Zhe Beihang University School of Mechanical Engineering & Automation Beijing China

ISBN: (纸本)9798350376289

To address the time-consuming issue of finding relevant design knowledge in product design, this study proposes a context-based adaptive online design knowledge push method. This method constructs a multi-dimensional design context model that includes task context models and user context models and proposes an update mechanism for these models. A database containing the mapping relationships and mapping values between knowledge ontologies and knowledge context models is established. Based on this, the TF-IDF algorithm is used to calculate the matching values between the required design knowledge context model and the actual design task context. Combining the mapping values of knowledge ontologies and knowledge context models, the relevance between the knowledge ontology and the actual design task is calculated. A knowledge list is generated in descending order of relevance and pushed to the user. Finally, this method is applied to a product adaptive online design technology platform, enabling the push of the most relevant knowledge to specific users at the right time, thereby reducing the time designers spend on knowledge retrieval and improving design efficiency and quality. © 2024 ieee.

关键词： context models knowledge matching natural language processing Self-adaptive knowledge push

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Enhancing Small Model Performance in Educational Classification Tasks through knowledge Distillation

Enhancing Small Model Performance in Educational Classificat...

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

作者： Xu, Haoxin Qi, Changyong Jiang, Bingqian Liu, Tong Zheng, Longwei Gu, Xiaoqing Shanghai Institute of AI for Education East China Normal University Shanghai China Department of Education Information Technology East China Normal University Shanghai China The School of Journalism and Communication Shanghai International Studies University Shanghai China School of Education City University of Macau China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798350368741

As the demand for precision, efficiency, and low-cost solutions in educational classification tasks continues to grow, enhancing model performance has become a critical focus of research. While large language models excel in these tasks, their high cost and resource requirements limit widespread application. This study proposes a knowledge-Enhanced Distillation (KED) method, utilizing ChatGPT-4, ChatGPT-4o, and Llama3 as teacher models, and three different sizes of BERT models as student models. The method was validated across three real-world educational datasets. The results demonstrate that the KED method significantly improves the accuracy and F1 scores of small models in educational text classification tasks, while also substantially reducing computational costs and resource consumption. Notably, the KED method shows exceptional performance in scenarios involving few-shot learning and class imbalance. The innovation of this study lies in applying the KED method to educational classification tasks, filling a gap in current research and highlighting its significant potential for practical application in educational contexts. © 2025 ieee.

关键词： Educational Application Few-Shot Learning knowledge Distillation Large language Model

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LLMs for knowledge-Graphs Enhanced Task-Oriented Dialogue Systems: Challenges and Opportunities 1

引用

36th international conference on Advanced Information Systems engineering (CAiSE)

作者： Iga, Vasile Ionut Remus Silaghi, Gheorghe Cosmin Babes Bolyai Univ Business Informat Res Ctr Cluj Napoca Romania

ISBN: (数字)9783031610035

ISBN: (纸本)9783031610028;9783031610035

Large language Models are a great tool for solving diverse tasks formulated in natural language. Recent work has demonstrated their capacity of solving tasks related to knowledge Graphs, such as knowledge Graph Completion or knowledge Graph Reasoning, even in Zero- or Few-Shot paradigms. However, given a particular input, they do not always produce the same output, and sometimes point to intermediate reasoning steps that are not valid, even if they produce a satisfactorily answer. Moreover, the use of LLMs is mostly studied for static knowledge graphs, while temporal ones are overlooked. To highlight opportunities and challenges in knowledge graph related tasks, we experiment with ChatGPT on graph completion and reasoning for both static and temporal facets, using three different prompting techniques in Zero- and One-Shot contexts, on a Task-Oriented Dialogue system use case. Our results show that ChatGPT can solve given tasks, but mostly in a nondeterministic way.

关键词： Large language Models knowledge Graph knowledge Graph Completion knowledge Graph Reasoning Task-Oriented Dialogue System

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Spatiotemporal-Aware Visual Captioning using Vision-language Pre-Training Model

Spatiotemporal-Aware Visual Captioning using Vision-Language...

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

作者： Wu, Shuai Yang, Weidong Wu, Shuyan School of Computer Science Fudan University Shanghai China Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350368741

Current visual captioning technologies typically transform 3D/2D visual information into one-dimensional sequential data and employ language models to generate corresponding descriptions. This approach, however, compromises the spatiotemporal information in visual data, making it difficult for models to capture temporal variations and the relative spatial relationships between objects. To address this issue, we propose STPos-VC, a pre-trained vision-language model that maps visual information from the visual vector space to the textual vector space through a visual-text mapper and generates natural language descriptions using a decoder. The mapper incorporates three-dimensional rotational position encoding, which effectively preserves the relative spatiotemporal positional relationships. Furthermore, we pre-train the model on a mixed dataset comprising images and videos through a visual question-answering framework, enabling the model to perform well even with small sample sizes. Experimental results across multiple datasets demonstrate that, compared to existing methods, STPos-VC achieves superior performance in both general-purpose and domain-specific applications. © 2025 ieee.

关键词： Multimodality Pre-training Spatiotemporal position encoding Visual language Model

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language Detection Using natural language processing 9

Language Detection Using Natural Language Processing

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Rajanak, Yutika Patil, Ratna Singh, Yadvendra Pratap Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology AIML Department Uttar Pradesh Greater Noida India Manipal University Jaipur Computer Science and Engineering Department Rajasthan Jaipur India

ISBN: (纸本)9798350397376

natural language processing (NLP) is a method for correctly identifying text based on the provided content or topic matter. An extensive study will make it simple to interpret any language and comprehend what is being said. Despite the fact that NLP is a challenging technique, notable examples include Siri and Alexa. natural language detection allows us to determine the language being used in a given document. A Python-written model that has been utilised in this work can be used to analyse the basic linguistics of any language. The "words"that make up sentences are the essential building blocks of knowledge and its expression. Correctly identifying them and comprehending the situation in which they are used are essential. NLP steps in to help us in this circumstance by making it easier for us to identify the linguistics used in a particular piece of information, whether it be written or vocal. NLP gives computers the ability to understand human language and respond correctly, performing language detection for us. The current paper provides a summary of developments in tongue process, including analysis, establishment, various areas of rapid advancement in natural language processing research, development tools, and techniques. © 2023 ieee.

关键词： Machine learning

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RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large language Models 24

RecPrompt: A Self-tuning Prompting Framework for News Recomm...

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33rd ACM international conference on Information and knowledge Management (CIKM)

作者： Liu, Dairui Yang, Boming Du, Honghui Greene, Derek Hurley, Neil Lawlor, Aonghus Dong, Ruihai Li, Irene Univ Coll Dublin Dublin Ireland Univ Tokyo Tokyo Japan

ISBN: (纸本)9798400704369

News recommendations heavily rely on natural language processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large language Models (LLMs) like GPT-4 have shown promising performance in understanding natural language. However, the extent of their applicability to news recommendation systems remains to be validated. This paper introduces RecPrompt(1), the first self-tuning prompting framework for news recommendation, leveraging the capabilities of LLMs to perform complex news recommendation tasks. This framework incorporates a news recommender and a prompt optimizer that applies an iterative bootstrapping process to enhance recommendations through automatic prompt engineering. Extensive experimental results with 400 users show that RecPrompt can achieve an improvement of 3.36% in AUC, 10.49% in MRR, 9.64% in nDCG@5, and 6.20% in nDCG@10 compared to deep neural models. Additionally, we introduce Topic-Score, a novel metric to assess explainability by evaluating LLM's ability to summarize topics of interest for users. The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.

关键词： News Recommendation Automatic Prompt engineering Large language Models

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Curriculum for a New Five-Year Academic Program in Intelligent Systems engineering and Software engineering

Curriculum for a New Five-Year Academic Program in Intellige...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Solo, Ashu M. G. Maverick Trailblazers Inc Wilmington DE 19805 USA

ISBN: (纸本)9798350361513;9798350372304

This research paper proposes a curriculum for a five-year academic program with a bachelor's degree (honors) in intelligent systems engineering and software engineering and a master's degree in intelligent systems engineering and software engineering. This program includes courses in data structures, compiler design, operating system design, firmware design, database systems, computer graphics and virtual reality design, static and dynamic website design, development of chatbots and voice assistants, software engineering methodology, knowledge-based systems, fuzzy logic, neural networks, evolutionary computation, evolutionary multiojective optimization, machine learning, image processing, computer vision, pattern recognition, voice recognition, natural language processing, data science, control systems, intelligent control systems, robotics, digital signal processing, mathematics, engineering physics, biology, etc. These degrees will allow graduates to have a good understanding of all of the main branches of intelligent systems engineering and software engineering as well as other relevant subjects in electrical and computer engineering.

关键词： education intelligent systems computational intelligence soft computing artificial intelligence machine learning software development firmware development

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SLMP: A Scientific Literature Management Platform Based on Large language Models 15

SLMP: A Scientific Literature Management Platform Based on L...

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15th ieee international conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 ieee.

关键词： Question answering

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