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ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback

ARES: Alternating Reinforcement Learning and Supervised Fine...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Byun, Ju-Seung Chun, Jiyun Kil, Jihyung Perrault, Andrew Department of Computer Science and Engineering The Ohio State University United States

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) excel at comprehending human instructions and demonstrate remarkable results across a broad spectrum of tasks. Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF) further refine LLMs by aligning them with specific preferences. These methods primarily use ranking-based feedback for entire generations. With advanced AI models (Teacher), such as GPT-4 and Claude 3 Opus, we can request various types of detailed feedback that are expensive for humans to provide. We propose a two-stage algorithm ARES that Alternates REinforcement Learning (RL) and Supervised Fine-Tuning (SFT). First, we ask the Teacher to score how much each sentence contributes to solving the problem in a Chain-of-Thought (CoT). This sentence-level feedback allows us to consider individual valuable segments, providing more granular rewards for the RL procedure. Second, we ask the Teacher to correct wrong reasoning after the RL stage. The RL procedure requires substantial hyperparameter tuning and often generates errors such as repetitive words and incomplete sentences. With correction feedback, we stabilize the RL fine-tuned model through SFT. We conduct experiments on the multi-modal datasets scienceQA and A-OKVQA to demonstrate the effectiveness of our proposal. The ARES rationale achieves around 70% win rate compared to baseline models judged by GPT-4o. Additionally, we observe that the improved rationale reasoning leads to a 2.5% increase in inference answer accuracy on average for the multi-modal datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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Wavelet-Based Feature Extraction and RCNN for ECG Signal Classification

Wavelet-Based Feature Extraction and RCNN for ECG Signal Cla...

引用

2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Singh, Ranjana Gangwar, Sristi Bartwal, Shikha Tomar, Vivek Computer Science and Engineering Department Dehradun India

ISBN: (纸本)9798350302820

The proposed method in this paper is an innovative approach for combining Continuous Wavelet Transform (CWT), and Recurrent Convolutional Neural Network (RCNN) to analyze ECG signals. The proposed method's objective is to improve the precision of the ECG signal analysis by integrating the advantages of these two methods. Using CWT, the constituent frequency components of the ECG signals are separated and analyzed, while RCNN is used to classify ECG signals into different classes such as Normal or Abnormal. © 2023 IEEE.

关键词： Electrocardiograms

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Automated Fact Checking Using A Knowledge Graph-based Model 6

Automated Fact Checking Using A Knowledge Graph-based Model

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Kundu, Arghya Nguyen, Uyen Trang York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350344349

Misinformation is a growing threat to the economy, social stability, public health, democracy, and national security. One of the most effective methods to combat misinformation is fact checking. Fact checking is the process of verifying the factual accuracy of a statement or claim. Fact checkers employ rigorous methodologies to scrutinize claims, verify sources, and expose falsehoods. However, the huge volume of content circulating online makes it challenging for humans to identify misinformation manually. Automated tools can analyze large datasets to detect patterns in misinformation content, scaling up fact checking efforts. This paper proposes a knowledge graph-based fact checking model that uses two separate knowledge graphs, one containing true claims and the other, false claims. The model uses knowledge graph embeddings which are based on convolutional neural networks. The deep learning model is trained on the above two knowledge graphs to learn distinguishing patterns between true and false claims. Additionally, we employ explainable artificial intelligence (XAI) techniques to provide explanations for the model's classification, reducing cost of errors and increasing transparency and user trust in the system. © 2024 IEEE.

关键词： Large datasets

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Does computer-Based Learning Suits Children's Desire to Control Their Own Learning Experience in Ghana?

Does Computer-Based Learning Suits Children's Desire to Cont...

引用

2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Amponsah, Rebecca Adwoa Doe, Martin Sebuabe, Samuel Yao Asare, Michael Tetteh Arthur, John Kingsley Damoah, Dominic Valley View University Computer Science and Engineering Department Oyibi Ghana

ISBN: (纸本)9798350376838

In Ghana's educational sector, the integration of ICT has changed traditional pedagogical ways hence introducing computer-based learning (CBL) as a powerful tool in controlling children's desire to enhance their learning experience. This paper explores the involvement of CBL with children's intrinsic motivation to manage their own experience in learning. In achieving this, purposive sampling technique was used throughout, and the results obtained was subjected to a simple descriptive statistical analysis. The results show that CBL technologies suit the ability of children to control their learning experiences with immediate feedback, motivation, memory retention, and acquisition of new and general knowledge. © 2024 IEEE.

关键词： Federated learning

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Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Model

Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Mo...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sasidaran, Keerthana Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

Hate speech detection is crucial for secure online environments, especially with recent policy changes on social media. This study enhances the Llama 2 model by fine-tuning it with a comprehensive Twitter dataset to improve hate speech detection accuracy. The system's frontend allows users to upload or record audio, upload videos, or provide YouTube URLs, with processing managed by the Google Speech Recognition API and the Whisper library. The backend integrates advanced speech recognition and video processing technologies. The refined Llama 2 model achieved an F1 score of 0.94, with accuracy, precision, and recall metrics surpassing other state-of-the-art models. These results demonstrate the model's high effectiveness and reliability in hate speech detection. This research highlights the potential of transformer-based models in enhancing online safety and inclusivity. Future work will focus on expanding the dataset, improving model generalization, supporting additional languages, and enhancing system scalability to better handle evolving hate speech forms. © 2024 IEEE.

关键词： Social networking (online)

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Ovulytics: A Machine Learning Approach for Precision Diagnosis of PCOD, PCOD and Infertility 1

Ovulytics: A Machine Learning Approach for Precision Diagnos...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jha, Tanushree Sirisha, M. Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCDS) and Polycystic Ovarian Disease (PCOD) are common endocrine disorders that affect a significant percentage of women of reproductive age, often leading to complications such as infertility. Early detection and differentiation of these conditions are important for timely intervention and effective management. This research proposes a machine learning-based solution to classify and predict the occurrence of PCDS, PCOD, and PCDS with infertility using Random Forest, Logistic Regression, and XGBoost. Each algorithm is trained and tested to predict the level of presence of PCDS, PCOD, and infertility-related PCDS. Among the models, Random Forest demonstrates superior performance. However, the importance of our work extends beyond performance metrics. This study illustrates how machine learning can be applied to complex, real-world healthcare challenges, such as early diagnosis and differentiation of overlapping conditions like PCDS and PCOD. The ability to leverage machine learning in this context aids earlier detection of these conditions, allowing for timely interventions that can have the potential to prevent further complications, such as infertility, cardiovascular diseases, and metabolic disorders. The potential impact of this research is substantial, offering scalable, non-invasive, and cost-effective diagnostic solutions, thus improving outcomes for millions of women worldwide and transforming reproductive healthcare practices. © 2024 IEEE.

关键词： Logistic regression

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Quantification of Reliability in Object-Oriented Software: Functionality Perspective 1

Quantification of Reliability in Object-Oriented Software: F...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Yadav, Anuj Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science Lucknow India

ISBN: (纸本)9798350373042

Object-oriented design and development are now commonly used in software development environments. Important design concepts including inheritance, coupling, cohesion, and encapsulation are supported by this method. The work in progress aims at offering a functionally-based, robust estimating technique for object-oriented design. Functionality plays a critical part in producing high-quality products within predetermined time and cost limits. This paper introduces an approach for quantifying functionality and reliability, acknowledging the paramount importance of functionality during the design phase. Statistical analyses affirm functionality as a significant factor influencing software reliability. Building on the proposed mapping of object-oriented design features to functionality, a multiple regression equation has been constructed to calculate the functionality of design hierarchies. It is clear that usefulness has a favourable effect on object-oriented systems' dependability. In order to determine dependability based on functionality, an additional multiple regression equation is created. Lastly, experimental trials are used to validate the suggested model. © 2024 IEEE.

关键词： Software reliability

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Bridging the Knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset 37

Bridging the Knowledge Gap: Improving BERT models for answer...

引用

37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

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A Comprehensive Study: Evaluating Machine Learning Algorithms with Credit Card Transaction Data 3rd

A Comprehensive Study: Evaluating Machine Learning Algorithm...

引用

3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Basak, Sarnali Shanto, Shaikh Qutub Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9789819739363

The financial sector places significant emphasis on the detection of credit card fraud, and machine learning techniques have emerged as a promising solution. Nevertheless, questions regarding the interpretability of machine learning models and their resilience against adversarial attacks within the realm of credit card fraud detection persist. This study aims to bridge these gaps and underscore their pivotal role in advancing the field. It involves a comprehensive analysis of a credit card transaction dataset and the development of multiple machine learning models for fraud detection. Python scripts leverage well-known libraries such as Pandas, NumPy, Seaborn, Scikit-Learn, TensorFlow, and Matplotlib for data processing, visualization, and machine learning tasks. For fraud detection, the dataset is partitioned into training and testing sets, and three machine learning models, namely XGBoost, Random Forest, and Support Vector Machine (SVM), are applied. Performance metrics, encompassing accuracy, sensitivity (recall), F1-score, Matthews Correlation Coefficient (MCC), Balanced Classification Rate (BCR), and confusion matrices, are computed and presented. Additionally, a Long Short-Term Memory (LSTM) neural network is deployed for sequence-based fraud detection. This research offers a comprehensive roadmap for analyzing credit card transaction data and constructing machine learning models to detect fraudulent activities, underscoring the importance of data exploration and model evaluation in fraud detection systems. It emphasizes the significance of feature engineering in enhancing the effectiveness of fraud detection models and equips decision-makers with the ability to make informed choices based on model predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Advance Approaches in AI-Powered Website Development for Educational Delivery via Ed-Tech 2

Advance Approaches in AI-Powered Website Development for Edu...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pandey, Hardik Kaswan, Suresh Bhardwaj, Hrishabh Pal, Priyansh Kumar, Aditya Amita Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350384277

This electronic document is a review paper on website development and the evolution of education into online education. We will discuss the current situation and speculations for the future of education and how can technology help shape it. Some recap of the evolution of education system will also be examined. This comprehensive review explores the dynamic intersection of website development, educational technology, and the evolving landscape of education. The studies encompass a range of topics, from addressing shortcomings in information systems and proposing innovative solutions like web-based alumni databases to examining unfulfilled promises in educational technology and the persistent challenges facing global education. Additionally, experiences in teaching highlevel web development courses during the Web 2.0 era and the increasing financial investment in educational technology are analyzed. The exploration of students' experiences in learning web development sheds light on the complexities and opportunities within these interconnected domains. Together, these diverse perspectives contribute to a nuanced understanding of the ongoing evolution in educational practices, technology integration, and the potential for transformative advancements. © 2024 IEEE.

关键词： Web Design

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