43 citations · 51 across the 14 of their papers we have counts for
12 papers · 1 filter
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
Zhengyuan Liu, Stella Xin Yin, Min-Yen Kan +1
We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaborat…
Contextual Representation Flow Across Depth in State-Space and Transformer Architectures
Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen +3
State Space Models (SSMs) have emerged as efficient, linear-time alternatives to Transformer-Based Models (TBMs) for long-context processing. However, their different mechanisms ra…
What Makes a Good Natural Language Prompt?
Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen +4
As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However,…
Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines
Do Xuan Long, Duong Ngoc Yen, Do Xuan Trong +5
In-context learning (ICL) is an important yet not fully understood ability of pre-trained large language models (LLMs). It can greatly enhance task performance using a few examples…
Reasoning Robustness of LLMs to Adversarial Typographical Errors
Esther Gan, Yiran Zhao, Liying Cheng +5
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. However, CoT can be biased by users' instruction. In thi…
Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models
Do Xuan Long, Duong Ngoc Yen, Anh Tuan Luu +3
We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides…
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
Do Xuan Long, Hai Nguyen Ngoc, Tiviatis Sim +5
We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach distinguishes between two categories of an evaluation…
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
Minzhi Li, Zhengyuan Liu, Shumin Deng +3
The acceleration of Large Language Models (LLMs) research has opened up new possibilities for evaluating generated texts. They serve as scalable and economical evaluators, but the…
Aligning Large Language Models with Human Opinions through Persona Selection and Value--Belief--Norm Reasoning
Do Xuan Long, Kenji Kawaguchi, Min-Yen Kan +1
Reasoning and predicting human opinions with large language models (LLMs) is essential yet challenging. Current methods employ role-playing with personae but face two major issues:…
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation
Minzhi Li, Taiwei Shi, Caleb Ziems +4
Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (L…
QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
Liangming Pan, Xinyuan Lu, Min-Yen Kan +1
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking system…
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
Chuang Li, Yan Zhang, Min-Yen Kan +1
Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain. We transform zero-shot DST into few-shot DST b…