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On The Decomposability Of Fractional Allocations, Shurojit Chatterji, Peng Liu Dec 2024

On The Decomposability Of Fractional Allocations, Shurojit Chatterji, Peng Liu

Research Collection School Of Economics

A common practice in dealing with the allocation of indivisible objects is to treat them as infinitely divisible and specify a fractional allocation, which is then implemented as a lottery on integer allocations that are feasible. The question we study is whether an arbitrary fractional allocation can be decomposed as a lottery on an arbitrary set of feasible integer allocations. The main result is a characterization of decomposable fractional allocations, that is obtained by transforming the decomposability problem into a maximum flow problem. We also provide a separate necessary condition for decomposability.

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Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang Dec 2024

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

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A Data-Driven Approach For Automated Multi-Site Competitive Facility Location, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau Dec 2024

A Data-Driven Approach For Automated Multi-Site Competitive Facility Location, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper addresses the challenge of optimal retail expansion in competitive urban environments through a novel approach to the Competitive Facility Location (CFL) problem. Traditional methods for solving CFL problems often struggle with large-scale scenarios, relying on manual pre-selection of candidate sites and imposing limitations on the number of new locations. Our approach leverages Adaptive Large Neighborhood Search (ALNS) enhanced with data enrichment techniques, including community detection on road networks and population weighting based on mobility data. We developed two ALNS variants: Community Geometric Centroid (CGC-ALNS) and Population Weighted Centroid (PWC-ALNS). These methods automate site selection, eliminating manual pre-selection while …

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Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou Dec 2024

Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou

Research Collection School Of Computing and Information Systems

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-toimage Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model’s cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate …

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4-Bit Shampoo For Memory-Efficient Network Training, Sike Wang, Pan Zhou, Jia Li, Hua Huang Dec 2024

4-Bit Shampoo For Memory-Efficient Network Training, Sike Wang, Pan Zhou, Jia Li, Hua Huang

Research Collection School Of Computing and Information Systems

Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise in reducing memory usage. However, current approaches only pertain to first-order optimizers. In this paper, we propose the first 4-bit second-order optimizers, exemplified by 4-bit Shampoo, maintaining performance similar to that of 32-bit ones. We show that quantizing the eigenvector matrix of the preconditioner in 4-bit Shampoo is remarkably better …

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Unified Generative And Discriminative Training For Multi-Modal Large Language Models, Wei Chow, Juncheng Li, Kaihang Pan, Qifan Yu, Hao Fei, Zhiqi Ge, Shuai Yang, Siliang Teng, Hanwang Zhang, Qianru Sun Dec 2024

Unified Generative And Discriminative Training For Multi-Modal Large Language Models, Wei Chow, Juncheng Li, Kaihang Pan, Qifan Yu, Hao Fei, Zhiqi Ge, Shuai Yang, Siliang Teng, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input …

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Sampdetox : Black-Box Backdoor Defense Via Perturbation-Based Sample Detoxification, Yanxin Yang, Chentao Jia, Dengke Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen Dec 2024

Sampdetox : Black-Box Backdoor Defense Via Perturbation-Based Sample Detoxification, Yanxin Yang, Chentao Jia, Dengke Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen

Research Collection School Of Computing and Information Systems

The advancement of Machine Learning has enabled the widespread deployment of Machine Learning as a Service (MLaaS) applications. However, the untrustworthy nature of third-party ML services poses backdoor threats. Existing defenses in MLaaS are limited by their reliance on training samples or white-box model analysis, highlighting the need for a black-box backdoor purification method. In our paper, we attempt to use diffusion models for purification by introducing noise in a forward diffusion process to destroy backdoors and recover clean samples through a reverse generative process. However, since a higher noise also destroys the semantics of the original samples, it still …

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Sprinql : Sub-Optimal Demonstrations Driven Offline Imitation Learning, Minh Huy Hoang, Tien Mai, Pradeep Varakantham Dec 2024

Sprinql : Sub-Optimal Demonstrations Driven Offline Imitation Learning, Minh Huy Hoang, Tien Mai, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

We focus on offline imitation learning (IL), which aims to mimic an expert's behavior using demonstrations without any interaction with the environment. One of the main challenges in offline IL is the limited support of expert demonstrations, which typically cover only a small fraction of the state-action space. While it may not be feasible to obtain numerous expert demonstrations, it is often possible to gather a larger set of sub-optimal demonstrations. For example, in treatment optimization problems, there are varying levels of doctor treatments available for different chronic conditions. These range from treatment specialists and experienced general practitioners to less …

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A Community-Driven Framework To Evaluate Factors Influencing Nft Buyers, Yi Meng Lau, Ping Fan Ke Dec 2024

A Community-Driven Framework To Evaluate Factors Influencing Nft Buyers, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

This study examines the pivotal role of community-driven social dynamics in shaping non-fungible tokens (NFT) buyers’ purchasing intentions and the increasing social significance of digital assets. Building on prior research in online word-of-mouth, we propose a comprehensive framework that integrates technological, personal, social, economic, and political factors influencing NFT purchases. The framework emphasizes the importance of service providers, personalization, perceived value, and associated risks, while also providing insights into how online word-of-mouth and community dynamics impact these aspects. As a preliminary exploration, this research lays the foundation for future empirical studies to validate the framework and assess its applicability across …

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Harnessing The Power Of Ai-Instructor Collaborative Grading Approach: Topic-Based Effective Grading For Semi Open-Ended Multipart Questions, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang Dec 2024

Harnessing The Power Of Ai-Instructor Collaborative Grading Approach: Topic-Based Effective Grading For Semi Open-Ended Multipart Questions, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Semi open-ended multipart questions consist of multiple sub questions within a single question, requiring students to provide certain factual information while allowing them to express their opinion within a defined context. Human grading of such questions can be tedious, constrained by the marking scheme and susceptible to the subjective judgement of instructors. The emergence of large language models (LLMs) such as ChatGPT has significantly advanced the prospect of automatic grading in educational settings. This paper introduces a topic-based grading approach that harnesses LLM capabilities alongside a refined marking scheme to ensure fair and explainable assessment processes. The proposed approach involves …

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Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan Dec 2024

Self-Supervised Fine-Tuning For Neural Expert Finding, Budhitama Subagdja, Dan Sanchari, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Expert finding systems allow ones to find individuals who have expertise in specific fields or domains. Traditional expert finding are mostly based on topic modeling or keyword search methods that are limited in their capability to encode contextual knowledge from natural language. To address the limitation, this paper presents Neural Expert Finder (NEF), a novel method that takes a transfer learning approach based on transformer encoder networks to leverage the rich seman-tic and syntactic patterns of language encoded in pre-trained language models (PLMs). We propose a self-supervised learning approach utilizing contrastive training using both positive and automatically generated negative samples …

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Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu Dec 2024

Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID …

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Improving Environment Novelty Quantification For Effective Unsupervised Environment Design, Jayden Teoh, Wenjun Li, Pradeep Varakantham Dec 2024

Improving Environment Novelty Quantification For Effective Unsupervised Environment Design, Jayden Teoh, Wenjun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student’s ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent’s optimal and actual performance, to guide curriculum design. Regret-driven methods generate curricula that progressively increase environment complexity for the student but overlook environment novelty–a critical element for enhancing an agent’s generalizability. Measuring environment novelty is especially challenging due to the underspecified …

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Revisiting Masked Auto-Encoders For Ecg-Language Representation Learning, Hung Manh Pham, Aaqib Saeed, Dong Ma Dec 2024

Revisiting Masked Auto-Encoders For Ecg-Language Representation Learning, Hung Manh Pham, Aaqib Saeed, Dong Ma

Research Collection School Of Computing and Information Systems

We propose C-MELT, a novel framework for multimodal self-supervised learning of Electrocardiogram (ECG) and text encoders. C-MELT pre-trains a contrastive-enhanced masked auto-encoder architecture using ECG-text paired data. It exploits the generative strengths with improved discriminative capabilities to enable robust cross-modal alignment. This is accomplished through a carefully designed model, loss functions, and a novel negative sampling strategy. Our preliminary experiments demonstrate significant performance improvements with up to 12% in downstream cardiac arrhythmia classification and patient identification tasks. Our findings demonstrate C-MELT's capacity to extract rich, clinically relevant features from ECG-text pairs, paving the way for more accurate and efficient cardiac …

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Generative Artificial Intelligence In Business Higher Education: A Focus Group Study, Xuenan Huo, Keng Siau Dec 2024

Generative Artificial Intelligence In Business Higher Education: A Focus Group Study, Xuenan Huo, Keng Siau

Research Collection School Of Computing and Information Systems

This research investigates the opportunities and challenges of integrating generative artificial intelligence (GenAI) into business higher education, drawing insights from an asynchronous focus group research study with doctoral students who serve dual roles as both learners and educators. Key opportunities identified through thematic analysis include knowledge acquisition, intelligent co-ideation, supportive augmentation, and personalized learning. Challenges identified include AI trustworthiness, cognitive dependency, human value, policy and instruction, assessment integrity, and identity management. This study clarifies GenAI’s specific role in business education and provides practical insights for effectively integrating GenAI to enhance learning outcomes and address emerging challenges. An analysis theory on …

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Gtree: Gpu-Friendly Privacy-Preserving Decision Tree Training And Inference, Qifan Wang, Shujie Cui, Lei Zhou, Ye Dong, Jianli Bai, Yun Sing Koh, Giovanni Russello Dec 2024

Gtree: Gpu-Friendly Privacy-Preserving Decision Tree Training And Inference, Qifan Wang, Shujie Cui, Lei Zhou, Ye Dong, Jianli Bai, Yun Sing Koh, Giovanni Russello

Research Collection School Of Computing and Information Systems

Outsourcing Decision tree (DT) training and inference to cloud platforms raises privacy concerns. Recent Secure Multi-Party Computation (MPC)-based methods are hindered by heavy overhead. Few recent studies explored GPUs to improve MPC-protected deep learning, yet integrating GPUs into MPC-protected DT with massive data-dependent operations remains challenging, raising question: can MPC-protected DT training and inference fully leverage GPUs for optimal performance?We present GTree, the first scheme that exploits GPU to accelerate MPC-protected secure DT training and inference. GTree is built across 3 parties who jointly perform DT training and inference with GPUs. GTree is secure against semi-honest adversaries, ensuring that no …

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Lilac: Log Parsing Using Llms With Adaptive Parsing Cache, Zhihan Jiang, Jinyang Liu, Zhuangbin Chen, Yichen Li, Junjie Huang, Yintong Huo, Pinjia He, Jiazhen Gu, R. Michael Lyu Dec 2024

Lilac: Log Parsing Using Llms With Adaptive Parsing Cache, Zhihan Jiang, Jinyang Liu, Zhuangbin Chen, Yichen Li, Junjie Huang, Yintong Huo, Pinjia He, Jiazhen Gu, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised due to the use of human-crafted rules or learning-based models with limited training data. The recent emergence of powerful large language models (LLMs) demonstrates their vast pre-trained knowledge related to code and logging, making it promising to apply LLMs for log parsing. However, their lack of specialized log parsing capabilities currently hinders their parsing accuracy. Moreover, the inherent inconsistent answers, as well as the …

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From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang Dec 2024

From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang

Research Collection School Of Computing and Information Systems

This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where …

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Harnessing Collective Structure Knowledge In Data Augmentation For Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen Dec 2024

Harnessing Collective Structure Knowledge In Data Augmentation For Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with …

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Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen Dec 2024

Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen

Research Collection School Of Computing and Information Systems

With the popularity of on-demand ride services worldwide, ride-sourcing platforms must maintain an adequate fleet size and cope with growing travel demand. Recently, platforms have attempted to provide vehicle rental services to drivers who do not own cars, then recruited them to provide on demand ride services. This helps lower the entry barrier for drivers and offers another profitable business for platforms. From the government's perspective, however, it is challenging to coordinately regulate a ride-sourcing business and vehicle rental business. This paper proposes a bi-level optimization model to investigate how the government regulates the ride-sourcing market integrated with vehicle rental …

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Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference, Xinrui Tan, Hongjia Li, Xiaofei Xie, Lu Guo, Nirwan Ansari, Xueqing Huang, Liming Wang, Zhen Xu, Yang Liu Dec 2024

Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference, Xinrui Tan, Hongjia Li, Xiaofei Xie, Lu Guo, Nirwan Ansari, Xueqing Huang, Liming Wang, Zhen Xu, Yang Liu

Research Collection School Of Computing and Information Systems

The recent advances of deep learning in various mobile and Internet-of-Things applications, coupled with the emergence of edge computing, have led to a strong trend of performing deep learning inference on the edge servers located physically close to the end devices. This trend presents the challenge of how to meet the quality-of-service requirements of inference tasks at the resource-constrained network edge, especially under variable or even bursty inference workloads. Solutions to this challenge have not yet been reported in the related literature. In the present paper, we tackle this challenge by means of workload-adaptive inference request scheduling: in different workload …

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Editorial For The Special Issue Of The Metaverse, Fiona Fui-Hoon Nah, Gert-Jan De Vreede, Lakshmi Goel, Eric Lim, Shu Schiller, Chee-Wee Tan Dec 2024

Editorial For The Special Issue Of The Metaverse, Fiona Fui-Hoon Nah, Gert-Jan De Vreede, Lakshmi Goel, Eric Lim, Shu Schiller, Chee-Wee Tan

Research Collection School Of Computing and Information Systems

The metaverse is laying the groundwork for more accessible and immersive experiences by blending the physical and virtual worlds into a unified space where people can interact, create, and connect in entirely new ways. It holds the potential to revolutionize how we work, socialize, and learn, which in turn gives rise to unprecedented opportunities for innovation. In this special issue, we present four articles that depict the current state of research in metaverse, the key themes and theoretical underpinnings within this space, as well as emerging directions for future work. This special issue delivers valuable insights for both researchers and …

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Universal Return And Factor Timing, Poh Ling Neo, Chyng Wen Tee, Jeroen Kerkhof Dec 2024

Universal Return And Factor Timing, Poh Ling Neo, Chyng Wen Tee, Jeroen Kerkhof

Research Collection Lee Kong Chian School Of Business

The authors highlight the shortcomings of commonly used performance graphs in assessing and comparing investment returns. This paper's main conceptual contribution is the introduction of Universal Return (UR) - a visual method to evaluate and rank investment strategies that challenges the traditional focus on raw performance statistics. The authors argue that the ranking of investment performance when viewed from a pool of investors over different entry time is more important than its overall absolute returns, as it better aligns investment analysis with the needs of investors, who are primarily concerned with identifying currently successful strategies or managers rather than dwelling …

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Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang Nov 2024

Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang

Research Collection School Of Computing and Information Systems

Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …

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Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong Nov 2024

Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract the internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user …

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Elevating Automated Software Maintenance Tasks With Large Language Models, Xin Zhou Nov 2024

Elevating Automated Software Maintenance Tasks With Large Language Models, Xin Zhou

Dissertations and Theses Collection (Open Access)

Software engineering involves many tasks across different phases such as requirements, design, implementation, testing, and maintenance. Among them, software maintenance is a crucial phase, typically accounting for more than half of the software life cycle's duration.
To boost developer productivity, in recent years, numerous research endeavors in software engineering have sought to automate certain software maintenance tasks through the application of machine learning techniques.
Since 2020, the emergence of advanced Large Language Models (LLMs) of code has opened new avenues for enhancing automated solutions in software maintenance.
This dissertation presents a series of works aimed at advancing automated solutions for …

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Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun Nov 2024

Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being adopted in a wide range of realworld applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) …

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Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua Nov 2024

Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event …

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Balancing Visual Context Understanding In Dialogue For Image Retrieval, Zhaohui Wei, Lizi Liao, Xiaoyu Du, Xinguang Xiang Nov 2024

Balancing Visual Context Understanding In Dialogue For Image Retrieval, Zhaohui Wei, Lizi Liao, Xiaoyu Du, Xinguang Xiang

Research Collection School Of Computing and Information Systems

In the realm of dialogue-to-image retrieval, the primary challenge is to fetch images from a pre-compiled database that accurately reflect the intent embedded within the dialogue history. Existing methods often overemphasize inter-modal alignment, neglecting the nuanced nature of conversational context. Dialogue histories are frequently cluttered with redundant information and often lack direct image descriptions, leading to a substantial disconnect between conversational content and visual representation. This study introduces VCU, a novel framework designed to enhance the comprehension of dialogue history and improve cross-modal matching for image retrieval. VCU leverages large language models (LLMs) to perform a two-step extraction process. It …

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Padlet As A Tool For Collaborative And Personalized Learning For University Courses, Yi Meng Lau, Wee Ling Loo, Bei Hong, Yuanto Kusnadi Nov 2024

Padlet As A Tool For Collaborative And Personalized Learning For University Courses, Yi Meng Lau, Wee Ling Loo, Bei Hong, Yuanto Kusnadi

Research Collection School Of Computing and Information Systems

This study investigates the effectiveness of Padlet, a technology-enhanced tool, that supports collaborative and personalized learning, and its success in meeting specific learning outcomes and course objectives. We developed a framework that synthesizes the learning characteristics of technology-enabled collaborative and personalized learning, integrating the strengths of both learning modes. Unlike other studies which typically focus on a single discipline, our framework is evaluated with the use of Padlet across four university courses, spanning three disciplines (Information Systems, Economics and Law). Survey results showed that Padlet enhanced both learning types and achieved the desired outcomes. Linear regression analysis underscored Padlet's effectiveness …

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