Barid Xi Ai | Web IR NLP Group @ NUS

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!

LitePruner project awarded VIP@SoC 10K development grant

Thu, 25 Sep 2025 00:00:00 +0000

Sahej’s LitePruner wins a Venture Intiation Programme award of 10,000 SGD to prototype and build out a prototype for commercialisation and use in production large language models!

LitePruner compresses LLM prompts to cut costs and latency. It works across APIs without retraining and preserves accuracy. A prototype targets high-volume enterprise users. Sahej is supervised by Dr Barid Xi Ai.

For the VIP, Sahej will be mentored by Prof. Yan Shuicheng.

About the Venture Initiation Programme at the School of Computing:

Is your idea unique or something that has been tried before? (Check out the past projects here for reference)

Is there a competitive or differentiating edge? How is it different from what already exists and how is it better?

Is there a sizable market and a clearly defined initial target user group for your proposed innovation?

Have you done sufficient research on the market need and competition?

Does the team have the competencies to deliver?

Are you able to demonstrate some early prototypes? (This would be an advantage!)

iGyro Symposium on Mis-, Dis-, and Mal-information (iGYRO SMDM 2025)

Mon, 28 Apr 2025 00:00:00 +0000

Demonstration of Multilingual QA Check to NUS President Tan Eng Chye

Thu, 20 Mar 2025 00:00:00 +0000

NUS President Tan Eng Chye visited the Centre for Trusted Internet and Community, and took part in hands-on demonstrations by members of the iGryo research team. WING-NUS demonstrated our Multilingual QA Check system, which performs Retrieval Augmented Generation and has multilingual capabilities to check claims in local Southeast Asian languages as well as in code-switched (mixed) sentences."

Is Knowledge in Multilingual Language Models Cross-Lingually Consistent?

Wed, 01 Jan 2025 00:00:00 +0000

Few works study the variation and cross-lingual consistency of factual knowledge embedded in multilingual models. However, cross-lingual consistency should be considered to assess cross-lingual transferability, maintain the factuality of the model knowledge across languages, and preserve the parity of language model performance. We are thus interested in analyzing, evaluating, and interpreting cross-lingual consistency for factual knowledge. We apply interpretability approaches to analyze the behavior of a model in cross-lingual contexts, discovering that multilingual models show different levels of consistency, subject to language families or linguistic factors. In addition, we identify a bottleneck in cross-lingual consistency on a particular layer. To mitigate this problem, we try vocabulary expansion, adding biases from monolingual inputs, and different types of supervision consisting of additional cross-lingual word alignment objective, instruction tuning, and code-switching training. We find that among these methods, code-switching training and cross-lingual word alignment objective show the most promising results, emphasizing the noteworthiness of cross-lingual alignment supervision for cross-lingual consistency enhancement.

M^3Check: A Demonstration System for Multilingual Multi-clue Multi-Hop Fact-Checking

Wed, 01 Jan 2025 00:00:00 +0000

Imagine that, while we cannot simply Google a complex claim, we can still find some relevant clues to help us. The question is, how can we use these clues to verify the input claim? M^3Check is a RAG model that follows three major steps: 1) break down the claim into clues, 2) verify each clue based on articles, and 3) conclude, check, and reason the claim based on a structured, supervised, and verified chain of clues. This project leverages findings from multiple projects, offering a good example of how research outcomes can be applied to address a real-world problem.

Model-generated Code-mixed Sentences

Wed, 01 Jan 2025 00:00:00 +0000

Code-switching/code-mixing is a common linguistic practice where speakers switch between multiple languages in a single discourse. Developing language models with code-switching capabilities is crucial to the interest of the large multilingual communities. However, collecting real-world code-mixed sentences for training remains challenging due to its colloquial nature, highlighting the importance of synthetic code-mixed data. Preliminary experiments shows LLMs can generate code-mixed sentences by switching entities in multiple languages. However, these model-generated sentences are often not natural limiting further leverage for training. This project explores methods to improve the naturalness of model-generated code-mixed sentences. Ultimately, we aim to build an automated pipeline capable of generating natural-sounding code-switched sentences for further downstream tasks.