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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Enhancing Multi-Person Dialogue with Large language Models: A Structured Approach to natural Communication 24

Enhancing Multi-Person Dialogue with Large Language Models: ...

引用

8th International conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Murogaki, Takumi Nishimura, Toshikazu Graduate School of Information Science and Engineering Ritsumeikan University Ibaraki Japan College of Information Science and Engineering Ritsumeikan University Ibaraki Japan

ISBN: (纸本)9798400717383

The rise of social networking services has increased text-based communication, often leading to misunderstandings. This study aims to develop a system using large language models (LLMs) like ChatGPT to provide real-time support in human dialogues. Traditional LLM chatbots, designed for one-on-one interactions, struggle with multi-person conversations, often leading to unnatural responses. This research proposes methods to enhance LLM's ability to distinguish between different speakers and improve its reasoning capabilities. By implementing "conversation structure tags"and simulating multi-person arguments, the system aims to generate natural, context-aware responses, enhancing the dialogue's quality and engagement. © 2024 Copyright held by the owner/author(s).

关键词： Speech enhancement

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Anomaly Detection on Real-time Security Log using Stream processing 17

Anomaly Detection on Real-time Security Log using Stream Pro...

引用

17th International Joint Symposium on Artificial Intelligence and natural language processing (iSAI-NLP) / 3rd International conference on Artificial Intelligence and Internet of Things (AIoT)

作者： Limprasert, Wasit Jantana, Patcharapon Liangsiri, Avirut Thammasat Univ Coll Interdisciplinary Studies Pathum Thani Thailand Storemesh Res Pathum Thani Thailand Bay Comp Nonthaburi Thailand

ISBN: (数字)9781665457279

ISBN: (纸本)9781665457279

Many critical tasks such as document approval and banking services, which are now hosted on cloud infrastructure. This transformation introduces stress on cloud security from the physical layer of the data center to the application layer of web application. All data access and service access need to be monitored and responded to in real-time. In this paper, we study methods to detect anomaly incidents such as spikes from network volume, malicious incidents from API scanning, error messages from internal systems and timeout from Slowloris attack[1]. We select machine learning based anomaly detection algorithms, such as LOF, Isolation Forest and Elliptic Envelope, to find suitable methods to detect incidents in real-time using stream processing tools including Kafka and message ingression. The result shows that LOF is fast and robust in most of the cases. However, when log messages have unseen words, which normally need to be hashed to preprocess, the Isolation Forest shows better results. This study shows the possibility of applying stream processing with machine learning to detect anomaly behavior for cloud services.

关键词： Anomaly Detection Stream processing Security Log Log Analysis

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Revisiting Checkpoint Averaging for Neural Machine Translation 2

Revisiting Checkpoint Averaging for Neural Machine Translati...

引用

2nd conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint conference on natural language processing, AACL-IJCNLP 2022

作者： Gao, Yingbo Herold, Christian Yang, Zijian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781959429043

Checkpoint averaging is a simple and effectivemethod to boost the performance of convergedneural machine translation models. The calculation is cheap to perform and the fact thatthe translation improvement almost comes forfree, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean ofthe model parameters from several checkpoints,the selection of which is mostly based on empirical recipes without many justifications. In thiswork, we revisit the concept of checkpoint averaging and consider several extensions. Specifically, we experiment with ideas such as usingdifferent checkpoint selection strategies, calculating weighted average instead of simplemean, making use of gradient information andfine-tuning the interpolation weights on development data. Our results confirm the necessityof applying checkpoint averaging for optimalperformance, but also suggest that the landscape between the converged checkpoints israther flat and not much further improvementcompared to simple averaging is to be obtained. © AACL-IJCNLP *** rights reserved

关键词： Neural machine translation

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IDE: A Multi-Agent-Driven Iterative Framework for Dynamic Evaluation of LLMs

IDE: A Multi-Agent-Driven Iterative Framework for Dynamic Ev...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xin Tong Bo Jin Jingya Wang Wenpeng Xing Tian Xia Meng Han People’s Public Security University of China Beijing China The Third Research Institute of the Ministry of Public Security of China China Zhejiang University Hangzhou China Renmin University of China Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the widespread use of large language models (LLMs) in natural language processing, traditional evaluation methods based on static datasets have become inadequate to fully capture their performance and generalization capabilities. To address this challenge, we propose an Iterative Dynamic Evaluation (IDE) framework, which utilizes a multi-agent system to systematically evaluate LLMs. The innovation of IDE lies in its iterative enhancement and comparative selection mechanisms. Through successive rounds of data augmentation, the framework emulates evolutionary processes by progressively increasing sample complexity, while competitive selection ensures only the optimal samples are retained. This iterative optimization and filtering produce increasingly challenging and diverse datasets. Experimental results demonstrate that IDE more effectively exposes the limitations of models in complex tasks compared to traditional methods, providing a more comprehensive foundation for the evaluation, optimization, and application of LLMs.

关键词： Technological innovation System performance Scalability Refining Signal processing natural language processing Iterative methods Speech processing Optimization Multi-agent systems

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MULTIMODAL ARCHITECTURE: Applications of language in a Machine Learning Aided Design Process 28th

MULTIMODAL ARCHITECTURE: Applications of Language in a Machi...

引用

28th International conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023

作者： Guida, George Harvard University Graduate School of Design United States

ISBN: (纸本)9789887891802

Recent advances in natural language processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process. © 2023 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： User interfaces

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Scene Graph Generation with Role-Playing Large language Models 38

Scene Graph Generation with Role-Playing Large Language Mode...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Chen, Guikun Li, Jin Wang, Wenguan Zhejiang University China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an Jiaotong University China Changsha University of Science & Technology China

Current approaches for open-vocabulary scene graph generation (OVSGG) use vision-language models such as CLIP and follow a standard zero-shot pipeline - computing similarity between the query image and the text embeddings for each category (i.e., text classifiers). In this work, we argue that the text classifiers adopted by existing OVSGG methods, i.e., category-/part-level prompts, are scene-agnostic as they remain unchanged across contexts. Using such fixed text classifiers not only struggles to model visual relations with high variance, but also falls short in adapting to distinct contexts. To plug these intrinsic shortcomings, we devise SDSGG, a scene-specific description based OVSGG framework where the weights of text classifiers are adaptively adjusted according to the visual content. In particular, to generate comprehensive and diverse descriptions oriented to the scene, an LLM is asked to play different roles (e.g., biologist and engineer) to analyze and discuss the descriptive features of a given scene from different views. Unlike previous efforts simply treating the generated descriptions as mutually equivalent text classifiers, SDSGG is equipped with an advanced renormalization mechanism to adjust the influence of each text classifier based on its relevance to the presented scene (this is what the term "specific" means). Furthermore, to capture the complicated interplay between subjects and objects, we propose a new lightweight module called mutual visual adapter. It refines CLIP's ability to recognize relations by learning an interaction-aware semantic space. Extensive experiments on prevalent benchmarks show that SDSGG outperforms top-leading methods by a clear margin. © 2024 Neural information processing systems foundation. All rights reserved.

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An empirical Study on Sentimental Analysis using Deep Learning

An Empirical Study on Sentimental Analysis using Deep Learni...

引用

Applied Artificial Intelligence and Computing (ICAAIC), International conference on

作者： Supriya Sameer Nalawade Arun S. Patil Electronics Department Sanjay Ghodawat University Kolhapur Kolhapur Maharashtra India Sanjay Ghodawat University Kolhapur Kolhapur Maharashtra India

A study of a person's attitude in terms of using several unstructured texts is denoted as Sentimental analysis or opinion mining. Opinion mining or sentimental analysis distinguishes as the degree of polarity discover. The estimation of tweet review topics and a product is a high-grade sentimental analysis. natural language understanding was essential for such data; many challenges were present in the natural language processing field for sentimental analysis. Nowadays, many pieces of research consider deep learning-based techniques for sentimental analysis in the natural language processing field. In this study, 25 papers were reviewed through deep learning-based approaches. Measures, as well as achievements attained by various methods, were simplified. The survey described the improvements and a limitation of each method as well as it regards the challenges and future potential research which is to acquire high accuracy and precision in sentimental analysis. Taxonomy represents the study gap and it elaborates on the various approaches.

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19th International conference on Software Technologies, ICSOFT 2024

19th International Conference on Software Technologies, ICSO...

引用

19th International conference on Software Technologies, ICSOFT 2024

ISBN: (纸本)9789897587061

The proceedings contain 57 papers. The special focus in this conference is on Software Technologies. The topics include: Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts;diagnosis Automation Using Similarity Analysis: Application to Industrial Systems;improving Robustness of Satellite Image processing Using Principal Component Analysis for Explainability;multimodal Approach Based on Autistic Child Behavior Analysis for Meltdown Crisis Detection;Towards Accurate Cervical Cancer Detection: Leveraging Two-Stage CNNs for Pap Smear Analysis;feature Extraction, Learning and Selection in Support of Patch Correctness Assessment;Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction;RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning;optimizing Intensive Database Tasks Through Caching Proxy Mechanisms;An Evaluation of Risk Management Standards and Frameworks for Assuring Data Security of Medical Device Software AI Models;a Systematic Mapping Study on Impact Analysis;HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection;automated Generation of Web Application Front-end Components from User Interface Mockups;a Webcam Artificial Intelligence-Based Gaze-Tracking Algorithm;an empirical Examination of the Technical Aspects of Data Sovereignty;asmDocGen: Generating Functional natural language Descriptions for Assembly Code;Integrating a LLaMa-based Chatbot with Augmented Retrieval Generation as a Complementary Educational Tool for High School and College Students;artificial Intelligence-Based Detection and Prediction of Giant African Snail (Lissachatina Fulica) Infestation in the Galapagos Islands;six-Layer Industrial Architecture Applied to Predictive Maintenance;Smart Blockchain-Based Information Flow Control for SOA;logging Hypercalls to Learn About the Behavior of Hyper-V.

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OrdinalCLIP: Learning Rank Prompts for language-Guided Ordinal Regression 36

OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordin...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Li, Wanhua Huang, Xiaoke Zhu, Zheng Tang, Yansong Li, Xiu Zhou, Jie Lu, Jiwen Tsinghua Univ Beijing Peoples R China PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9781713871088

This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts. These methods are easy to overfit and usually attain unsatisfactory performance as the learned concepts are mainly derived from the training set. Recent large pre-trained vision-language models like CLIP have shown impressive performance on various visual tasks. In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space. Specifically, we reformulate this task as an image-language matching problem with a contrastive objective, which regards labels as text and obtains a language prototype from a text encoder for each rank. While prompt engineering for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists of learnable context tokens and learnable rank embeddings. The learnable rank embeddings are constructed by explicitly modeling numerical continuity, resulting in well-ordered, compact language prototypes in the CLIP space. Once learned, we can only save the language prototypes and discard the huge language model, resulting in zero additional computational overhead compared with the linear head counterpart. Experimental results show that our paradigm achieves competitive performance in general ordinal regression tasks, and gains improvements in few-shot and distribution shift settings for age estimation. The code is available at https://***/xk-huang/OrdinalCLIP.

关键词： Embeddings

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A Contrastive Framework for Neural Text Generation 36

A Contrastive Framework for Neural Text Generation

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Su, Yixuan Lan, Tian Wang, Yan Yogatama, Dani Kong, Lingpeng Collier, Nigel Univ Cambridge Language Technol Lab Cambridge England Tencent AI Lab Shenzhen Peoples R China DeepMind London England Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781713871088

Text generation is of great importance to many natural language processing applications. However, maximization-based decoding methods (e.g., beam search) of neural language models often lead to degenerate solutions-the generated text is unnatural and contains undesirable repetitions. Existing approaches introduce stochasticity via sampling or modify training objectives to decrease the probabilities of certain tokens (e.g., unlikelihood training). However, they often lead to solutions that lack coherence. In this work, we show that an underlying reason for model degeneration is the anisotropic distribution of token representations. We present a contrastive solution: (i) SimCTG, a contrastive training objective to calibrate the model's representation space, and (ii) a decoding method-contrastive search-to encourage diversity while maintaining coherence in the generated text. Extensive experiments and analyses on three benchmarks from two languages demonstrate that our proposed approach significantly outperforms current state-of-the-art text generation methods as evaluated by both human and automatic metrics.

关键词： Decoding

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