Sahej Agarwal | Web IR NLP Group @ NUS

WING 2026 Spring Outing at Clarke Quay

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

WING members were excited to have a spring outing at Clarke Quay on January 23, 2026. For this outing, we scheduled it at the beginning of the semester to help new members better cohere with the group.

We enjoyed both the beautiful river view and the pizza! We were happy to see the return of our alumni, especially Hengchang, Victor, and Taha, as well as our collaborator, Yunshan. We especially thank Hengchang (a WING PhD alumnus) for his generous donation of the pizza (and other items on the bill as well)!

Below is a gallery of the outing, photo credit to Yajing, Glenda, and Yisong!

WING Members Enjoy the Group Outing at the Macritchie Reservoir

Fri, 14 Nov 2025 00:00:00 +0000

A gallery of additional photos of the outing is included below.

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

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!)

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.