Zihang Fu | Web IR NLP Group @ NUS

LLM-Augmented Health Community Notes

Sun, 01 Nov 2026 00:00:00 +0000

Community Notes can provide context for misleading health posts, but useful corrections often arrive late. We study how large language models can support timely, evidence-grounded correction: first by augmenting and automating individual notes, and then by reusing experience from previous cases to improve future notes. The project connects two complementary studies of health misinformation governance on social platforms.

In Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation (ACL 2026), we analyze 30.8K health-related notes and find a median delay of 17.6 hours before notes receive a helpfulness status. We introduce CrowdNotes+, which combines evidence-grounded note augmentation with utility-guided note automation. Its hierarchical evaluation separates relevance, correctness, and helpfulness, addressing the tendency to mistake fluent writing for factual accuracy. HealthNotes, a benchmark of 1.2K health notes annotated for helpfulness, supports this study. The CrowdNotes+ repository provides the code and data.

In Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes (Findings of EMNLP 2026), we investigate how note generation can improve across correction episodes. EvoNote distills feedback from completed trajectories into an evolving experience memory that guides claim analysis, evidence acquisition, and note writing. We evaluate it on MM-HealthCN, a 1.2K-instance multimodal benchmark with human-written notes and crowd-derived helpfulness labels. This extends the project’s focus from improving individual corrections to learning reusable correction strategies. The EvoNote repository provides the implementation and MM-HealthCN data.

Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes

Thu, 01 Oct 2026 00:00:00 +0000

Why Is This Joke Offensive? Interpreting Offensiveness in Chinese Dark Humor Through Knowledge and Rhetoric

Thu, 01 Oct 2026 00:00:00 +0000

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

Wed, 01 Jul 2026 00:00:00 +0000

WING Hosts a Lab Tour for AAAI 2026 Participants

Fri, 23 Jan 2026 00:00:00 +0000

AAAI 2026 has been held in Singapore from January 20 to January 27. Some of our group members are excited to travel to the conference site (at Expo) to communicate research. In conjunction with our regular group meeting on Friday, January 23, we are delighted to invite AAAI participants to visit our WING lab and experience the NUS Computing environment.

With our visitors, this group meeting had a special agenda:

Minzhi Li from WING NUS gave a presentation to debrief her 2025 internship experience.

Dr. Lu Gan (from GESIS - Leibniz Institute for the Social Sciences) gave a presentation about an AAAI paper from their team, titled “GSAP-ERE: Fine-Grained Scholarly Entity and Relation Extraction Focused on Machine Learning” (available here).

Finally, we had a round of self-introductions for all WING members as well as the visitors to socialize and discuss research.

Below is a gallery of the lab tour, photo credit to Sahaj, Yisong, and Min!

WING's CS6101 Retrieval Augmented Generation comes to a successful close at 27th STePS

Wed, 12 Nov 2025 00:00:00 +0000

WING ran an experimental section of our CS6101 Exploration of Computer Science Research, as a trial course on Retrieval Augmented Generation.

RAG is now a common gateway experience for the general public as well as students to experience Information Retrieval, a core research area of WING.

The CS6101 course website has publicly-available lecture slides, video lecture recordings and scribe notes for the class’s topics.

These include sections on LLM generation, Retreival, Ranking, RAG Models, Iterative Retrieval and Advanced RAG topics (inclusive of graph, multimodal RAG) and source credibility estimation.

Check out the LinkedIn posts by class participants Vangmay Sachin, Jonathan Chen and Sahej Agrawal.

You can also visit the other WING Events, including past public iterations of WING’s CS6101 sections.

Image enhanced and regenerated via Google’s Nano Banana