<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Anh Tuan Luu | Web IR NLP Group @ NUS</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/author/anh-tuan-luu/</link><atom:link href="https://www.comp.nus.edu.sg/~kanmy/wing/author/anh-tuan-luu/index.xml" rel="self" type="application/rss+xml"/><description>Anh Tuan Luu</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 01 Aug 2025 00:00:00 +0000</lastBuildDate><image><url>https://www.comp.nus.edu.sg/~kanmy/wing/media/icon_hub1b49484bccf4f586453e7d2a784a896_225489_512x512_fill_lanczos_center_3.png</url><title>Anh Tuan Luu</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/author/anh-tuan-luu/</link></image><item><title>Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/publication/long-etal-2025-beyond/</link><pubDate>Fri, 01 Aug 2025 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/publication/long-etal-2025-beyond/</guid><description/></item><item><title>A Comparative Analysis of Representation Flow in State-Space and Transformer Architectures</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/project/ssm-interpretability/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/project/ssm-interpretability/</guid><description>&lt;p>State-space models (SSMs) have emerged as promising alternatives to Transformers, particularly for long-context tasks, due to their efficiency in modeling long-range dependencies through structured state transitions. While prior work has focused on interpreting final-layer outputs, this study investigates the feature flow across layers in SSMs. By comparing this behavior to Transformers, we show fundamental differences in how contextual information is encoded and propagated. Our analysis reveals trade-offs in efficiency and expressivity, offering a deeper understanding of learning dynamics in both architectures. This work not only advances our understanding of SSMs but also lays the foundation for designing hybrid models that combine the strengths of both paradigms.&lt;/p></description></item><item><title>Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/publication/long-etal-2024-multi-expert/</link><pubDate>Thu, 12 Dec 2024 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/publication/long-etal-2024-multi-expert/</guid><description/></item><item><title>SSNLP '24</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/event/ssnlp24/</link><pubDate>Wed, 06 Nov 2024 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/event/ssnlp24/</guid><description/></item><item><title>ToXCL: A Unified Framework for Toxic Speech Detection and Explanation</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/publication/hoang-etal-2024-toxcl/</link><pubDate>Tue, 26 Mar 2024 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/publication/hoang-etal-2024-toxcl/</guid><description/></item><item><title>ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Question</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/publication/vanlong-2024-chatgpt/</link><pubDate>Wed, 28 Feb 2024 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/publication/vanlong-2024-chatgpt/</guid><description/></item><item><title>Fact-Checking Complex Claims with Program-Guided Reasoning</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/publication/pan-etal-2023-fact/</link><pubDate>Sat, 01 Jul 2023 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/publication/pan-etal-2023-fact/</guid><description/></item></channel></rss>