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Ansible Lightspeed: A Code Generation Service for IT Automation 24

Ansible Lightspeed: A Code Generation Service for IT Automat...

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

作者： Sahoo, Priyam Pujar, Saurabh Nalawade, Ganesh Gebhardt, Richard Mandel, Louis Buratti, Luca Red Hat Raleigh NC USA IBM Res Yorktown Hts NY 10598 USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The availability of Large language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for Information Technology (IT) automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Ansible Lightspeed is an LLM-based service designed explicitly to generate Ansible YAML, given natural language prompt. In this paper, we present the design and implementation of the Ansible Lightspeed service. We then evaluate its utility to developers using diverse indicators, including extended utilization, analysis of user edited suggestions, as well as user sentiments analysis. The evaluation is based on data collected for 10,696 real users including 3,910 returning users. The code for Ansible Lightspeed service and the analysis framework is made available for others to use. To our knowledge, our study is the first to involve thousands of users of code assistants for domain-specific languages. We are also the first code completion tool to present N-Day user retention figures, which is 13.66% on Day 30. We propose an improved version of user acceptance rate, called Strong Acceptance rate, where a suggestion is considered accepted only if less than 50% of it is edited and these edits do not change critical parts of the suggestion. By focusing on Ansible, Lightspeed is able to achieve a strong acceptance rate of 49.08% for multi-line Ansible task suggestions. With our findings we provide insights into the effectiveness of small, dedicated models in a domain-specific context. We hope this work serves as a reference for software engineering and machine learning researchers exploring code c

关键词： Productivity Sentiment analysis Codes Automation Large language models natural languages Machine learning Software Software engineering Domain specific languages

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Cross-Lingual Sentiment Prediction with Various Classifiers for Structural Correspondence Learning and Synonym Augmentation 1

Cross-Lingual Sentiment Prediction with Various Classifiers ...

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1st international conference for Women in Computing, InCoWoCo 2024

作者： Meghana, D.K. Kiran, K. Harshala, R. Shenoy, P. Deepa Venugopal, K.R. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The Cross-Lingual Sentiment Analysis(CLSA) is a process of utilizing information from resource rich language to predict sentiment of resource poor languages. The monolingual approaches based on machine learning cannot be viably applied for resource-scarce domain as sufficient information is not available. This has picked up specific interest in the field on natural language processing. Subsequently, this article proposes Structural Correspondence Learning (SCL) that utilizes basic correspondence for cross-lingual opinion investigation with synonyms. The key thought behind SCL, is to exploit the knowledge from the source language space to target language space by minimizing loss of data[1]. In contrast to previous SCL methods where it uses logistic classifier to predict, here different classifiers like Random Forest, Gaussian Naïve Bayes, Bernoulli NB, Multinomial NB, K- nearest Neighbors and Decision Tree are trained. This work comprises of two categories: one, training the model with synonyms of pivot for different classifiers and another one, training the model without synonyms on target language using different classifiers. The experimental results show that target language appended with synonyms performs better compared to the one without synonyms for Random Forest. © 2024 ieee.

关键词： Contrastive Learning

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M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary Semantic Segmentation 2

M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary...

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2nd international conference on Algorithm, Image processing and Machine Vision, AIPMV 2024

作者： Zhang, Yuhang Che, Yanqiu Li, Shanshan Wang, Ruofan School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798350390254

With the emergence of large-scale visual language models represented by CLIP, we have broken the great divide between vision and language. It has become possible to project features from visual models to semantic space consisting of a large amount of text. At the same time, semantic segmentation has opened the door to transferring learned visual knowledge to open vocabulary semantic segmentation. In this paper we improve and optimize a model with open vocabulary segmentation capability based on the CLIP architecture, we design an RM-MLP module and insert this module after the semantic group module in the image encoder, which is used to optimize the semantic regions aggregated by the semantic group module, and then used to generate the final segmentation results. The final experimental results show that the optimized model achieves higher segmentation accuracy on PASCAL VOC 2012 (+1.14% mIoU), and COCO (+0.43% mIoU). © 2024 ieee.

关键词： Semantic Segmentation

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Can Large language Models Grasp Event Signals? Exploring Pure Zero-Shot Event-based Recognition

Can Large Language Models Grasp Event Signals? Exploring Pur...

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

作者： Yu, Zongyou Qu, Qiang Chen, Xiaoming Wang, Chen School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China School of Computer Science The University of Sydney Sydney Australia

ISBN: (纸本)9798350368741

Recent advancements in event-based zero-shot object recognition have demonstrated promising results. However, these methods heavily depend on extensive training and are inherently constrained by the characteristics of CLIP. To the best of our knowledge, this research is the first study to explore the understanding capabilities of large language models (LLMs) for event-based visual content. We demonstrate that LLMs can achieve event-based object recognition without additional training or fine-tuning in conjunction with CLIP, effectively enabling pure zero-shot event-based recognition. Particularly, we evaluate the ability of GPT-4o/4turbo and two other open-source LLMs to directly recognize event-based visual content. Extensive experiments are conducted across three benchmark datasets, systematically assessing the recognition accuracy of these models. The results show that LLMs, especially when enhanced with well-designed prompts, significantly improve event-based zero-shot recognition performance. Notably, GPT-4o outperforms the compared models and exceeds the recognition accuracy of state-of-the-art event-based zero-shot methods on N-ImageNet by five orders of magnitude. © 2025 ieee.

关键词： event camera large language model object recognition

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Research on Deaf-Mute People Translation System Based on natural language processing 5

Research on Deaf-Mute People Translation System Based on Nat...

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5th international conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Zhang, Tianyi Liao, Sherry Shanghai No.8 High School Shanghai China Qsi International School Shenzhen China

ISBN: (纸本)9798331528911

Sign language is a vital mode of communication for deaf individuals, yet it frequently proves challenging to convey messages to hearing people. The preceding two-way interaction system was found to be ineffective. Furthermore, convolutional neural network-based devices are also susceptible to interference in complex environments, which affects the recognition effect. The objective of the model proposed in this work is to address the communication barriers between deaf-mute and hearing-friendly people, primarily through the utilisation of sign language interpretation. The initial stage of the process involved the utilisation of a convolutional neural network (CNN) to facilitate gesture recognition, capture the intricate details of sign language actions, and integrate deep learning methodologies to extract the distinctive features of gesture images. Subsequently, a natural language processing model is employed to combine the visual features with the language model, thereby facilitating accurate translation from sign language to spoken language. To enhance recognition capabilities in complex settings, the system employs multimodal data fusion technology, integrating gesture actions and contextual semantic data to bolster the reliability and precision of translation. The experimental analysis demonstrates that the model exhibits high accuracy and efficiency in translation and interaction across a range of scenarios. © 2024 ieee.

关键词： Convolutional neural networks

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ADT: An Additive Delta-Tuning approach for parameter-efficient tuning in pre-trained language models 6

ADT: An Additive Delta-Tuning approach for parameter-efficie...

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6th international conference on natural language processing (ICNLP)

作者： Li, Dong Tang, Jintao Li, Shasha Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China

ISBN: (纸本)9798350349122;9798350349115

The advent of pre-trained language models (PLMs) has revolutionized the field of natural language processing (NLP), enabling models to leverage vast amounts of language and world knowledge for various downstream tasks. However, the conventional fine-tuning of these models requires updating full parameters, which is computationally intensive and impractical in resource-constrained settings. Parameter-efficient tuning methods have been developed to mitigate this. However, these methods may need to be improved in terms of inference latency, optimization difficulty, and input sequence length reduction. In this paper, we introduce a new reparameterization-based parameter-efficient tuning method, Additive Delta Tuning (ADT). ADT only fine-tunes part of the row and column parameters in the PLM weight matrix. This approach significantly reduces the computational cost compared to existing methods, such as Low-Rank Adaptation (LoRA), while maintaining competitive performance. We conducted extensive experiments across multiple natural language understanding (NLU) tasks, including text classification, text entailment, named entity recognition, etc., to evaluate the effectiveness of ADT. Our experimental results demonstrate that ADT achieves comparable or better performance than traditional fine-tuning methods, requiring less than 0.3% parameter updates. This shows that ADT is an effective method for parameter-efficient tuning. Our method achieves comparable results to LoRA on multiple NLU tasks, with the added benefit of requiring less computational cost.

关键词： Parameter-Efficient Tuning Pre-trained language Models Additive Delta-Tuning LoRA

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Aligning the Representation of knowledge Graph and Large language Model for Causal Question Answering

Aligning the Representation of Knowledge Graph and Large Lan...

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2024 ieee international conference on Big Data, BigData 2024

作者： Zeng, Zefan Cheng, Qing Hu, Xingchen Liu, Zhong Shen, Jingke Zhang, Yahao Laboratory for Big Data and Decision National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350362480

Causal Question Answering (CQA) is essential for knowledge discovery, focusing on the intricate dynamics between events and entities without predefined contexts. Despite advancements of CQA models through knowledge Graphs (KGs) and Pre-Trained language Models (PLMs), existing approaches are hindered by knowledge conflict, insufficient capacity, and limitations in information fusion. Large language Models (LLMs) have significantly improved natural language understanding and reasoning but often suffer from causal hallucinations. To address these challenges, we introduce KLop, a framework that aligns representations of Causal knowledge Graph (CKG) and Large language Models for CQA. KLop pre-trains a graph embedding model for entity embedding and uses a frozen LLM for text embedding. The main components of KLop are the descriptor module and the aligner module. The descriptor leverages descriptive texts generated by LLMs to create training data for knowledge alignment, while the aligner utilizes self-attention to train query tokens for modality alignment. Experiments on public CQA datasets validate that KLop outperforms various advanced baselines in reasoning accuracy, as well as achieving causal knowledge integration and joint reasoning. © 2024 ieee.

关键词： knowledge graph

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A Complex Reasoning Framework for Intelligent Learning Assistant Based on Large language Model and knowledge Graph 4

A Complex Reasoning Framework for Intelligent Learning Assis...

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4th international Signal processing, Communications and engineering Management conference, ISPCEM 2024

作者： Yun, Xiao PBC School of Finance Tsinghua University Beijing China

ISBN: (纸本)9798331528676

Large language models are widely applied in educational scenarios. Students can engage in personalized and interactive learning through intelligent learning assistants installed on various devices. Among these applications, integrating large language models (LLMs) with knowledge graphs to achieve complex reasoning is a key issue. Traditional methods often solve multi-hop query problems based on individual technologies, leading to insufficient accuracy and comprehensiveness in answering complex questions. This paper proposes an intelligent agent framework called K12-Agent, which uses retrieval-enhanced generation, fine-tuning of LLMs integrated with knowledge graphs, and collaborative reasoning to enhance the initiative of the agent in interactions with students. Compared to traditional methods, K12-Agent has improved the accuracy by 15% and the recall rate by 13% when facing complex student questions. Additionally, K12-Agent is more adaptable to the expression methods of younger students, making it more user-friendly. ©2024 ieee.

关键词： knowledge graph

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Research on the Construction of Chinese Literature knowledge Graph and Intelligent Recommendation Algorithm

Research on the Construction of Chinese Literature Knowledge...

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2024 international conference on language Technology and Digital Humanities, LTDH 2024

作者： Zhang, Donglei Luo, Ye Li, Bingxian Weifang Engineering Vocational College Shandong China

ISBN: (纸本)9798350380187

The digitization trend of Chinese literature is becoming more and more significant, which provides literature lovers with more convenient ways of reading and rich literary resources. However, how to quickly and accurately find the works that meet personal interests and needs among the vast amount of literary works has become an urgent problem to be solved. Therefore, this paper focuses on the construction of Chinese literature knowledge-graph and intelligent recommend-algorithms, aiming to provide personalized reading recommendation services for literature lovers. Through natural language processing and text mining technology, a large number of Chinese literary works are deeply analyzed and structured to extract the key information in the literary works. This paper designs a variety of intelligent recommendation strategies based on the constructed knowledge-graph of Chinese literature. Combined with the classical recommend-algorithm, a hybrid recommend-algorithm based on knowledge-graph is designed. By training the neural network model, the mining and prediction of the user's potential interest is realized, which further ameliorates the intelligent level of recommendation. This study not only provides personalized reading recommendation service for Chinese literature lovers, but also provides new ideas and methods for the construction of Chinese literature knowledge-graph and the research of intelligent recommend-algorithms. ©2024 ieee.

关键词： knowledge graph

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Unlocking the Potential of Large language Models in Legal Discourse: Challenges, Solutions, and Future Directions 6

Unlocking the Potential of Large Language Models in Legal Di...

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6th international conference on Intelligent Computing in Data Sciences, ICDS 2024

作者： Kaoutar, M'Rhar Chaima, Ben Jaafar Omar, Bencharef Outmane, Bourkoukou Cadi Ayyad University Computer and System Engineering Laboratory FSTG Marrakesh Morocco

ISBN: (纸本)9798350351200

The rise of Large language Models (LLMs) has been remarkable, especially exemplified by the achievements of systems such as ChatGPT and Google's Bard. Both specialized and general users are warmly embracing these potent tools, indicating their increasing integration into everyday life. Nevertheless, challenges persist in their widespread adoption, particularly within specialized fields where they necessitate meticulous fine-Tuning and access to high-quality data. Additionally, their lack of interpretability further complicates matters, often relegating them to the status of 'black boxes'. Within the legal domain, LLMs harbor transformative potential but encounter obstacles due to legal hallucinations. This research delves into these hallucinations through a distinct set of legal queries pertaining to Canadian tax law, drawing comparisons between state-of-The-Art LLMs. Its objective is to illuminate their efficacy in legal discourse and specialized domains, capitalizing on their broad knowledge base. The research advocates for fine-Tuning as a potential solution, stressing the significance of domain-specific LLMs and delineating methods for their development. This includes considerations such as dataset curation, preprocessing techniques, model selection, and adherence to regulatory requirements, encompassing the creation of domain-specific vocabularies. Practical implementation entails the generation of domain-specific LLMs tailored for legal tasks such as research, information retrieval, and question answering. Despite inherent limitations, the study proposes avenues for enhancement and underscores the significance of LLMs utilization in legal services. This contributes to the evolution of natural language processing technology within the legal realm. © 2024 ieee.

关键词： natural language processing systems

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