Author: Charlotte Turner

Authors: Kangjun Noh、Baekryun Seong、Hoyoon Byun、Sungjin Song、Kyungwoo Song Paper: https://arxiv.org/abs/2408.10923 Introduction Large Language Models (LLMs) have revolutionized natural language processing (NLP) tasks, achieving remarkable success in areas such as response generation. However, their application to tabular data has been limited due to their relatively inferior performance compared to traditional machine learning models (TMLs) like XGBoost. This study introduces the Language-Based-Classifier (LBC), a novel approach that leverages the pre-trained knowledge of LLMs to handle Out-of-Variable (OOV) tasks effectively. OOV tasks involve scenarios where new variables appear in the test data that were not present during training. This capability is crucial in real-world…

Authors: Jianian Gong、Nachuan Duan、Ziheng Tao、Zhaohui Gong、Yuan Yuan、Minlie Huang Paper: https://arxiv.org/abs/2408.10495 Introduction The rapid advancement of large language models (LLMs) such as GPT-4 has revolutionized the landscape of software engineering, positioning these models at the core of modern development practices. As we anticipate these models to evolve into the primary and trustworthy tools used in software development, ensuring the security of the code they produce becomes paramount. This paper presents a systematic investigation into LLMs’ inherent potential to generate code with fewer vulnerabilities. Specifically, the study focuses on GPT-3.5 and GPT-4’s capability to identify and repair vulnerabilities in the code generated…

Authors: Xiao Han、Zijian Zhang、Xiangyu Zhao、Guojiang Shen、Xiangjie Kong、Xuetao Wei、Liqiang Nie、Jieping Ye Paper: https://arxiv.org/abs/2408.10286 Introduction The rapid growth of online ride-hailing services has revolutionized urban transportation, making vehicle dispatching a critical component in enhancing travel quality. However, existing vehicle dispatch systems face significant challenges due to the complexities of urban traffic dynamics, such as unpredictable traffic conditions, diverse driver behaviors, and fluctuating supply and demand patterns. These challenges often result in travel difficulties for passengers in certain areas and idle drivers in others, leading to a decline in the overall quality of urban transportation services. To address these issues, the paper introduces…

Authors: Jiajun Xu、Qun Wang、Yuhang Cao、Baitao Zeng、Sicheng Liu Paper: https://arxiv.org/abs/2408.10230 Introduction In recent years, Virtual Assistants (VAs) such as Amazon’s Alexa, Apple’s Siri, Google Assistant, and Microsoft’s Cortana have become integral to our daily lives, facilitating a range of services easily. Despite their widespread adoption, traditional VAs often struggle with processing complex commands and providing accurate responses. The recent emergence of Large Language Models (LLMs) like ChatGPT and Claude provide solutions to overcome these limitations and promise a new era of Intelligent Assistants (IAs) that are capable of interpreting intricate contexts and delivering more satisfactory responses. The popular trend of IAs…

Authors: Xinqi Su、Yawen Cui、Ajian Liu、Xun Lin、Yuhao Wang、Haochen Liang、Wenhui Li、Zitong Yu Paper: https://arxiv.org/abs/2408.10883 Introduction In today’s digital age, the rapid spread of fake news across online social networks (OSNs) like Twitter and Weibo poses significant threats to society. The proliferation of fake news not only increases the information burden but can also incite panic and lead to severe societal consequences. To combat this, automated fake news detection (FND) has become a critical area of research. This paper introduces a novel approach called Dynamic Analysis and Adaptive Discriminator (DAAD) for detecting fake news, particularly focusing on multimodal fake news detection (MFND), which…

Authors: Jun Yan、Pengyu Wang、Danni Wang、Weiquan Huang、Daniel Watzenig、Huilin Yin Paper: https://arxiv.org/abs/2408.09839 Introduction Background Semantic segmentation is a critical perception task in autonomous driving, enabling environmental perception, path planning, decision-making, barrier avoidance, collision prevention, precise localization, and human-computer interaction. The performance of semantic segmentation is crucial for ensuring the Safety of the Intended Functionality (SOTIF) in autonomous driving systems. Over the past decade, deep learning has significantly advanced semantic segmentation models, transitioning from convolutional neural networks (CNNs) to vision transformers (ViTs) and now to foundation models like the Segment-Anything Model (SAM). Problem Statement Despite the advancements, semantic segmentation models are vulnerable to…

Authors: Xinnan Dai、Qihao Wen、Yifei Shen、Hongzhi Wen、Dongsheng Li、Jiliang Tang、Caihua Shan Paper: https://arxiv.org/abs/2408.09529 Introduction Background Large Language Models (LLMs) have demonstrated remarkable success in various reasoning tasks, including mathematical problem-solving, commonsense reasoning, and symbolic problem-solving. However, their ability to handle graph reasoning tasks remains under scrutiny. Graph reasoning is crucial for applications such as question-answering systems, autonomous planning, and robot navigation. Despite theoretical studies suggesting that LLMs can handle graph reasoning tasks, empirical evaluations reveal numerous failures. Problem Statement This study aims to revisit the graph reasoning ability of LLMs by focusing on three fundamental graph tasks: graph description translation, graph connectivity,…

Authors: Bruno Amaral Teixeira de Freitas、Roberto de Alencar Lotufo Paper: https://arxiv.org/abs/2408.08925 Introduction The advent of Large Language Models (LLMs), particularly with the release of OpenAI’s GPT series, has revolutionized human-machine textual interactions. These models have enabled the creation of chat assistants, or chatbots, that can engage in more natural conversations and better understand user needs. When combined with Retrieval-Augmented Generation (RAG) techniques, these models can interact with external software systems, enhancing their capabilities with data retrieved from external sources. In the realm of e-commerce, the potential for such systems is immense. With global retail e-commerce sales projected to surpass 6.3…

Authors: Tongyoung Kim、Soojin Yoon、Seongku Kang、Jinyoung Yeo、Dongha Lee Paper: https://arxiv.org/abs/2408.08686 Introduction Sequential recommendation aims to predict a user’s next interaction by capturing the context from their interaction history. With the advancement of language models (LMs), recent progress in sequential recommender systems has been driven by utilizing LMs for their text understanding and generation capabilities. Generative retrieval approaches decode item identifiers based on user interaction history, leveraging LMs to generate sequences of indices for the target item. However, the integration of collaborative and semantic information in these systems has remained unexplored. In this paper, we propose SC-REC, a unified recommender system that…

Authors: Damiano Azzolini、Elisabetta Gentili、Fabrizio Riguzzi Paper: https://arxiv.org/abs/2408.08732 Introduction Statistical Relational Artificial Intelligence (StarAI) is a subfield of AI that focuses on describing complex probabilistic domains using interpretable languages. Examples of such languages include Markov Logic Networks, Probabilistic Logic Programs, and Probabilistic Answer Set Programs (PASP). This paper addresses the task of parameter learning within PASP, which involves tuning the probabilities of facts in a probabilistic logic program to maximize the likelihood of observed interpretations. The authors propose two algorithms for parameter learning in PASP: 1. An algorithm that uses an off-the-shelf constrained optimization solver. 2. An algorithm based on the…