Discourse | Web IR NLP Group @ NUS

SSNLP 2026: WING Members Present Three Posters

Wed, 21 Jan 2026 00:00:00 +0000

The Singapore Symposium on Natural Language Processing (SSNLP) returns on Wednesday, January 21, 2026, at SUTD as a full-day, in-person event. Since 2018, SSNLP has been the annual gathering of Singapore’s NLP community, bringing together students, faculty, and industry researchers to share ongoing work and spark collaboration. Held in conjunction with AAAI 2026, this year’s symposium will feature invited keynotes from leading researchers in the field, a panel discussion, and an immersive poster-focused program showcasing recent work from Singapore-based researchers published in top venues such as ACL, EMNLP, NeurIPS, ICLR, and AAAI this year (this short intro is propagated from SSNLP 2026 website).

WING members are delighted to attend the seminar, enjoying keynote talks from guest researchers who travel all the way from AoE to Singapore. We also appreciate the panel discussions led by PC Chairs (from *ACL, ICLR, and NeurIPS conference), who share valuable insights on the peer review process, particularly from the students’ perspective.

We also engage in the immersive poster program, and our group is excited to present three posters:

Discursive Circuits: How Do Language Models Understand Discourse Relations? by Yisong Miao and Min-Yen Kan (EMNLP 2025)
SkyLadder: Better and Faster Pretraining via Context Window Scheduling by Tongyao Zhu, Qian Liu, Haonan Wang, Shiqi Chen, Xiangming Gu, Tianyu Pang, and Min-Yen Kan (NeurIPS 2025)
Drifting Away from Truth: GenAI-Driven News Diversity Challenges LVLM-Based Misinformation Detection by Fanxiao Li, Jiaying Wu, Tingchao Fu, Yunyun Dong, Bingbing Song and Wei Zhou (AAAI 2026).

We thank the symposium organizers for the opportunity to present our work, as well as the enthusiastic audience who engaged with us during the poster sessions!

Below is a gallery of the poster presentations, we thank Prof Min for taking these photos!

Yisong presenting his work on “Discursive Circuits: How Do Language Models Understand Discourse Relations?”:

Tongyao presenting his work on “SkyLadder: Better and Faster Pretraining via Context Window Scheduling”:

Fanxiao presenting his work on “Drifting Away from Truth: GenAI-Driven News Diversity Challenges LVLM-Based Misinformation Detection”:

WING Members Present at NUS AI Seminar at the Computer Science Department

Wed, 19 Nov 2025 00:00:00 +0000

NUS AI Seminar at the Computer Science Department is a new initiative to bring together AI researchers to share their latest research findings and insights. There are eight speakers in the Fall 2025 semester, usually hosted on Wednesday afternoons at Seminar Room 21. WING members are excited to present their research alongside other presenters from NUS Computing, MIT, and UCLA. We appreciate Prof. Lee Wee Sun and PhD student Guoji Fu for hosting the seminar.

Yisong Miao gave a seminar presentation titled “Faithfulness and Interpretability for Discourse Understanding” on November 19th, 2025. This talk combines his work on Discursive Socratic Questioning (ACL 2024) and Discursive Circuits (EMNLP 2025). He explained how our team tackles the technical challenge of evaluating a model’s discourse faithfulness and formalizing a unique setup for transformer circuit discovery. The seminar ends with an audience discussion on the conceptual difference between truthfulness and faithfulness, as well as designing new methods using sparse circuits in future work.

Yajing Yang presented “From Data to Insights: LLMs for Financial Narration”, also on November 19th, 2025, highlighting her research on financial data narration. Drawing from DataTales: A Benchmark for Real-World Intelligent Data Narration (EMNLP 2024) and KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration (EMNLP 2025), she described our team’s efforts to build a realistic benchmark and improve generation quality through knowledge integration and hierarchical analysis. The talk sparked discussion on remaining challenges in financial report generation and how LLMs could be used to produce more actionable and informative insights.

On December 3rd, 2025, Xuan Long Do delivered a seminar titled “Toward Autonomous Self-Improving Foundation Models”. The presentation spanned multiple projects, including “What Makes a Good Natural Language Prompt?” (ACL 2025), Multi-Expert Prompting (EMNLP 2024), and VISTA, his 2025 Google internship project on a self-improving video generation agent. Beginning with an analysis of prompt and model fragility, he showed how multi-agent reasoning can help address these limitations before moving toward the broader vision of autonomous self-improving foundation models. The seminar concluded with an audience discussion on future research directions in this area.

Below is a gallery of the seminar photos, some of which are enhanced and regenerated via Google’s Nano Banana

Yisong Miao presenting his work on “Faithfulness and Interpretability for Discourse Understanding” at the AI Seminar on November 19th, 2025:

Yajing Yang presenting her work on “From Data to Insights: LLMs for Financial Narration” at the AI Seminar on November 19th, 2025:

Xuan Long Do presenting his work on “Toward Autonomous Self-Improving Foundation Models” at the AI Seminar on December 3rd, 2025:

WING Members Present at NUS Computing's Inaugural AI-X: Industry Day

Tue, 18 Nov 2025 00:00:00 +0000

A gallery of additional photos from the event is included below, with thanks to the official event photographer.

Discourse and LLMs

Tue, 04 Nov 2025 00:00:00 +0000

Large language models (LLMs) can now generate coherent text, but can they truly understand language? In this project, we explore discourse understanding, a foundational objective in natural language processing (NLP). In the first part, we refine the notion of understanding as faithful behavior under a series of Socratic-style questions (appeared at ACL 2024). In the second part, we take a causal approach to mechanistically interpret the key components of transformer models in discourse processing (appeared at EMNLP 2025).

Yisong, a PhD student at WING, began his research journey by tackling the fundamental question: Do language models truly understand text? Over time, he refined this broad inquiry into the specific challenge of discourse understanding. In his recent work, advised by Prof. Min, they introduced a more precise framework for measuring discourse comprehension from three discourse-specific perspectives, while also contributing insights into broader LLM challenges such as logical consistency. Currently, Yisong and Min are delving deeper into the mechanisms behind how models process and understand discourse, and they discovered that sparse computational graphs, termed as “discursive circuits”, control how models process discourse relations.

Discursive Circuits: How Do Language Models Understand Discourse Relations?

Tue, 04 Nov 2025 00:00:00 +0000

Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations

Sun, 11 Aug 2024 00:00:00 +0000