Digital Commons Network^™

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna

Research Collection Yong Pung How School Of Law

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensusbuilding between stakeholders (particularly government and industry but also citizens) andvoluntary or “quasi” regulation, …

How Can Chinese Culture Go Abroad? Evidence From Successful Establishment Of Chinese Academic Foreign Translation Projects, Tian Lan

Dissertations and Theses Collection (Open Access)

Compared to Chinese economy scale, the global understanding and recognition of Chinese culture are quite superficial, it is crucial to promote Chinese culture go abroad through academic exchanges between China and other countries. This study explores and validates the relationship and applicability of Chinese culture “Going Global” based on the translation project of Chinese academic works. Through case analysis and the development of data models, it aims to provide theoretical guidance and practical tools for relevant institutions and individuals.

The study begins with qualitative research on cases, describing and analyzing typical examples to demonstrate how Chinese Academic Translation (CAT) projects …

Interpreting Topic Models In Byte-Pair Encoding Space, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Byte-pair encoding (BPE) is pivotal for processing text into chunksize tokens, particularly in Large Language Model (LLM). From a topic modeling perspective, as these chunksize tokens might be mere parts of valid words, evaluating and interpreting these tokens for coherence is challenging. Most, if not all, of coherence evaluation measures are incompatible as they benchmark using valid words. We propose to interpret the recovery of valid words from these tokens as a ranking problem and present a model-agnostic and training-free recovery approach from the topic-token distribution onto a selected vocabulary space, following which we could apply existing evaluation measures. Results …

Adapting Installation Instructions In Rapidly Evolving Software Ecosystems, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

files play an important role in providing installation-related instructions to software users and are widely used in open source software systems on platforms such as GitHub. Software projects evolve rapidly alongside their dependencies in dynamic software ecosystems, requiring frequent updates to installation instructions. These instructions are crucial for users to start with a software project. Despite their significance, there is a lack of systematic understanding regarding the documentation efforts invested in README files and the triggers behind them. To fill the research gap, we conducted a qualitative study, investigating 400 GitHub repositories with 1,163 README commits that focused on updates …

The Gradient Puppeteer: Adversarial Domination In Gradient Leakage Attacks Through Model Poisoning, Kunlan Xiang, Haomiao Yang, Meng Hao, Shaofeng Li, Haoxin Wang, Zikang Ding, Wenbo Jiang, Tianwei Zhang

Research Collection School Of Computing and Information Systems

In Federated Learning (FL), clients share gradients with a central server while keeping their data local. However, malicious servers could deliberately manipulate the models to reconstruct clients' data from shared gradients, posing significant privacy risks. Although such Active Gradient Leakage Attacks (AGLAs) have been widely studied, they suffer from two severe limitations: 1) coverage: no existing AGLAs can reconstruct all samples in a batch from the shared gradients; 2) stealthiness: no existing AGLAs can evade principled checks of clients. In this paper, we address these limitations with two core contributions. First, we introduce a new theoretical analysis approach, which uniformly …

Diaspora Politics And A Rising China: Ambivalent Ties Amid Geopolitical Changes, Jiaqi M. Liu

Research Collection School of Social Sciences

Over the past decade, the rise of China on the global stage has witnessed the emergence of members of the Chinese diaspora as new meso-level actors in mediating Sino-Western relations. As part of China’s burgeoning “public diplomacy” around the globe, Chinese migrants organize cultural events to establish grassroots and semi-official exchanges between China and the West. This chapter provides a state-of-the-art examination of so-called “diasporic public diplomacy” whereby the Chinese diaspora plays an increasingly important role in promoting China’s soft power abroad. Based on long-term fieldwork in a prominent emigrant community, I analyse how Chinese national policies envision two specific …

A Power Dependence Model Of The Impact Of Leader Impostorism On Supervisor Support And Undermining: The Moderating Role Of Power Distance, Xueqi Wen, Zihan Liu, Feng Qiu, Keith Leavitt, Xingyu Wang, Ziyang Tang

Research Collection Lee Kong Chian School Of Business

Leaders, often perceived as possessing exceptional confidence and competence, are not immune to feelings of self-doubt. Leader impostorism describes the experience that one’s attributes, experiences, skills, and abilities fall short of the standards expected in the leadership role, resulting in a sense of deception in fulfilling leadership responsibilities. While existing research has examined the antecedents and individual outcomes of leader impostorism, its implications for leaders’ treatment of subordinates remain largely unexplored. In this research, we investigate the downstream consequences of leader impostorism on behaviors directed toward subordinates. Integrating research on leader impostorism with power dependence theory, we propose that for …

Path Nets: Concurrence And Recurrence In The Dynamics Of Organizing, Brian T. Pentland, Waldemar Kremser, Kenneth T. Goh

Research Collection Lee Kong Chian School Of Business

This article proposes a link between temporal structuring and the dynamics of organizing that is manifest in a fabric of concurrent paths that we call a path net. Path nets are shaped by mechanisms of temporal structuring, such as entrainment, planning, agency, and chance. Path nets materialize the effects of temporal structuring “here” and “now” in the comings and goings of actors and resources, thereby setting the stage for doings and sayings of situated practice and shaping the dynamics of organizing. Path nets offer a parsimonious system of picturing the complexity of organizing that is built on a processual ontology …

Risk And Crisis Communication Research In Singapore, Augustine Pang, Jerena Ng, Jason Shi-Yang Lim

Research Collection Lee Kong Chian School Of Business

This chapter aims to capture the latest insights on the state of risk and crisis communication research in Singapore, examined through the theoretical lens posited by Situational Crisis Communication Theory (SCCT) (Coombs, 2023). This chapter is divided into three sections: the first, consolidates risk and crisis communication research studies, the second examines the state of research, and the third offers a case study on the research conducted. Last but not least, we offer some suggestions on how research can grow. It is hoped that this chapter can offer a comprehensive view of risk and crisis communication research in Singapore and …

Financial Named Entity Recognition: How Far Can Llm Go?, Yi-Te Lu, Yintong Huo

Research Collection School Of Computing and Information Systems

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight …

Breaking The Language Barriers? Machine Translation Technology And Analysts’ Forecasts For Firms With Foreign Operations, Bingxu Fang, Pengkai Lin

Research Collection School Of Accountancy

We study the impact of machine translation technology on analysts’ forecasts for firms with foreign operations. Exploiting the staggered rollout of Google Translate’s support for translating foreign languages into English, we find that U.S. analysts improve their forecast accuracy for firms with substantial business exposure to the corresponding foreign countries. We obtain similar evidence using an international sample of analysts covering firms outside their home countries. The improvement in forecast accuracy is greater for analysts with limited language skills or brokerage resources to process foreign information. Further analysis suggests that analysts raise more questions about firms’ foreign exposure during conference …

Ai Characterisations And Their Legal Implications, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

This chapter examines the difficult legal characterisation problems that artificially intelligent systems raise and explores how different characterisations of artificial intelligence (AI) shape practical legal outcomes. Three reasons are offered for the legal difficulty with characterising AI. First, answers to characterisation problems are inherently subjective and perspective-driven, particularly when the subject is an intangible technological system. Second, AI technology is especially difficult to define since the field typically proceeds on inexact anthropomorphic metaphors. Third, AI characterisation problems raise difficult sub-problems, particularly in determining how autonomous an AI system is. The chapter thus argues that a range of plausible AI characterisations …

Constitutional Comparisons And Language, De Maartje Visser

Research Collection Yong Pung How School Of Law

Language can be seen as an intuitive challenge for those who embark on comparative constitutional research: its intricate entanglement with law is often overlooked and not engaged with explicitly. This chapter argues that this is a missed opportunity at best, and arguably presents a hazard for the collective quality of the scholarly discourse. It discusses four ways in which language matters when engaging in constitutional comparisons: the problem of literal translation; the problem of legal-conceptual translation; a multilingual discourse that is marked by fragmentation; and expectations in communication with a foreign or international readership. The approach taken is constructive, with …

Towards Unified Multimodal Editing With Enhanced Knowledge Collaboration, Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic …

Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expertdesigned contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, …

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …

Chain Of Preference Optimization: Improving Chain-Of-Thought Reasoning In Llms, Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved …

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 …

User Acceptance Of Advice By Ai Agents: Expectation-System Fit Perspective, Jingyuan Cai, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Algorithms have increasing influence on our daily decisions, especially when the recommendations are presented by human-like AI agents. This study applies the Theory of Effective Use to investigate how the fit between the user’s role expectation for an AI agent and the agent’s interaction style impacts AI advice adoption. We proposed a new concept termed Perceived Expectation-System Fit (PESF) and empirically examined its impact on user perceptions and advice acceptance. We found that low PESF reduces advice acceptance by diminishing cognitive and affective trust in the AI agent. Furthermore, increased algorithm transparency increases PESF's impact on decision-making. Our findings provide …

Lova3 : Learning To Visual Question Answering, Asking And Assessment, Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Bai Shou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3 , an innovative framework named “Learning tO Visual question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming …

Smart Heuristics In Business Relationships: Toward A Typology, Jochen Reb, Nilotpal Jha

Research Collection Lee Kong Chian School Of Business

Purpose – The purpose of this paper is to develop a typology of heuristics in business relationships. We distinguish between four categories: (1) general heuristics used in the context of a business relationship but that may also (and are often) used in other contexts; (2) relational context heuristics that are typically used in a relational context; (3) relational information heuristics that rely on relational information and (4) genuine relational heuristics that use relational information and are applied in relational contexts. Design/methodology/approach – We draw on existing literature on heuristics and business relationships to inform our conceptual paper. Findings – We …

Collaboration! Towards Robust Neural Methods For Routing Problems, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen

Research Collection School Of Computing and Information Systems

Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues – their performance significantly deteriorates on clean instances with crafted perturbations. To enhance robustness, we propose an ensemble-based Collaborative Neural Framework (CNF) w.r.t. the defense of neural VRP methods, which is crucial yet underexplored in the literature. Given a neural VRP method, we adversarially train multiple models in a collaborative manner to synergistically promote robustness against attacks, while boosting standard generalization on clean instances. A neural router is designed to adeptly distribute training instances among models, enhancing …

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 …

Spatial Trajectories, Theories, And Topoi In The Study Of Religion And Film, Teng-Kuan Ng

Research Collection College of Integrative Studies

This chapter explores the correlations between religion and film studies and the geographies of religion. Using perspectives from the latter to foreground overlooked aspects of the former, I argue that investigations of the religious problematic via film are productively enriched when refocused on the question: what kind of space does religious cinema constitute? Rather than offering a single answer, I highlight the various phenomenological, ritual, and ethical spaces that cinema—comprising production, text, and reception—can construct. In the first section, I briefly trace the “spatial turns” that religion and film scholarship has taken since its inception, observing the field’s evolving geographical …

Here, There, And Elsewhere: Ethnicity, Identity, And Global Orientation At An Indian International School In Singapore, Emma Alexandra Grimley, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

International schools are somewhat ‘place-less’ in that their denationalised educational systems and nationally diverse student bodies are typically removed from the physical context in which each school is located. However, this placeless internationalism is complicated by international schools that also affiliate themselves with a national system or enrol a significant number of students who aspire to remain in the ‘host’ country. This paper develops the example of the Global Indian International School in Singapore to illustrate how the feeling of place is impacted by its dual orientations as both ‘Global’ and ‘Indian.’ The school attempts to (re)create an Indian schooling …

Prevention Is Better Than Cure: Rethinking Court Behaviour And Design, De Maartje Visser

Research Collection Yong Pung How School Of Law

Using a relational dynamics perspective, this commentary considers how to buttress courts' ability to practice their role as guardians of the constitution. It suggests that we should pay more attention to empowering the general public to provide meaningful social support for courts through constitutional literacy efforts. Such literacy efforts should focus on the place of the court within the separation of powers and the value of judicial independence. At the same time, it may be prudent to redact the list of tasks given to constitutional courts and divest responsibility for some activities to other institutions.

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 …

Reevo: Large Language Models As Hyper-Heuristics With Reflective Evolution, Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, Guojie Song

Research Collection School Of Computing and Information Systems

The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design process. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present Reflective Evolution (ReEvo), a generic searching framework that emulates the reflective design approach of human experts while far surpassing human capabilities with its scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. Evaluations …

Citizen Science In A Small World Of Singlish Words, Cynthia S. Q. Siew, Jin Jye Wong

Singapore Open Research Conference 2024

Lightning talk “Citizen Science in A Small World of Singlish Words: A Word Association Study” by Winner of the Singapore Open Research Awards 2024, Assistant Professor Cynthia S. Q. SIEW from National University of Singapore. Abstract: Motivated by the lack of representation of non-dominant English dialects in existing psycholinguistic word norms, our project aims to create a database of semantic, affective, and word association norms for Singapore English words. Our research lab prioritises making all of our collected datasets and analysis scripts openly available through online repositories, such as Open Science Framework (OSF), ensuring reproducibility and reusability of research findings. …

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) …