7 papers
The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models
Xue Wen Tan, Nathaniel Tan, Galen Lee +1
Evaluating the quality of reasoning traces from large language models remains understudied, labor-intensive, and unreliable: current practice relies on expert rubrics, manual annot…
Anticipate, Simulate, Reason (ASR): A Comprehensive Generative AI Framework for Combating Messaging Scams
Xue Wen Tan, Kenneth See, Stanley Kok
The rapid growth of messaging scams creates an escalating challenge for user security and financial safety. In this paper, we present the \textit{Anticipate, Simulate, Reason} (ASR…
Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchical Transformer Network Approach
Xue Wen Tan, Stanley Kok
Every publicly traded U.S. company files an annual 10-K report containing critical insights into financial health and risk. We propose Tiny eXplainable Risk Assessor (TinyXRA), a l…
SMARTe: Slot-based Method for Accountable Relational Triple extraction
Xue Wen Tan, Stanley Kok
Relational Triple Extraction (RTE) is a fundamental task in Natural Language Processing (NLP). However, prior research has primarily focused on optimizing model performance, with l…
Prediction of Bank Credit Ratings using Heterogeneous Topological Graph Neural Networks
Junyi Liu, Stanley Kok
Agencies such as Standard & Poor's and Moody's provide bank credit ratings that influence economic stability and decision-making by stakeholders. Accurate and timely predictions su…
ScamGPT-J: Inside the Scammer's Mind, A Generative AI-Based Approach Toward Combating Messaging Scams
Xue Wen Tan, Kenneth See, Stanley Kok
The increase in global cellphone usage has led to a spike in instant messaging scams, causing extensive socio-economic damage with yearly losses exceeding half a trillion US dollar…