CL | Web IR NLP Group @ NUS

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.

Emoji Composition

Mon, 20 May 2024 00:00:00 +0000

The project began when Yisong, intrigued by the Emoji Mashup Bot, explored the question: Can emojis be composed to convey meanings like English phrases? With the support of his advisor, Prof. Min, he recruited an undergraduate student, Zi Yun Yang, to collaborate on the project. Ms. Yang built an annotation platform to collect emoji compositions and their corresponding English phrases from her peers. After her graduation, Yisong and the team added another layer of annotation on composition types and formalized an emoji-based textual entailment task (EmoTE).

The first iteration of this work was published at LREC-COLING 2024. Prof. Min was not listed as an author due to his role as a conference chair, but this project cannot be finished without his guidance. The project continues to gain interest, particularly among undergraduates, with many students from our local NLP course using the emoji dataset for their final projects.

Light Verb Constructions

Sat, 01 May 2004 00:00:00 +0000

Project Duration: May 2004 - July 2005.

A light verb construction (LVC) is a verb–complement pair in which the verb has little lexical meaning and much of the semantic content of the construction is obtained from the complement. Examples of LVCs are “make a decision” and “give a presentation”, and these pose challenges for natural language processing and understanding. In this project, we investigate methods to identify LVCs from a corpus, as well as recognizing linguistic features of LVCs.