4 citations · 13 across the 21 of their papers we have counts for
21 papers
Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs
Jingtan Wang, Arun Verma, Xiaoqiang Lin +4
How to divide a fixed annotation budget between supervised fine-tuning (SFT) and reinforcement learning (RL) during LLM post-training remains an open problem. Existing work charact…
How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
Jiangwei Chen, Xinyuan Niu, Rachael Hwee Ling Sim +3
Machine unlearning aims to remove the influence of specific forget training data due to privacy, copyright or bias concerns while maintaining the model performance on the remaining…
Small Changes, Big Impact: Demographic Bias in LLM-Based Hiring Through Subtle Sociocultural Markers in Anonymised Resumes
Bryan Chen Zhengyu Tan, Shaun Khoo, Bich Ngoc Doan +3
Large Language Models (LLMs) are increasingly deployed in resume screening pipelines. Although explicit PII (e.g., names) is commonly redacted, resumes typically retain subtle soci…
The Chicken and Egg Dilemma: Co-optimizing Data and Model Configurations for LLMs
Zhiliang Chen, Alfred Wei Lun Leong, Shao Yong Ong +6
Co-optimizing data and model configurations for training LLMs presents a classic chicken-and-egg dilemma: The best training data configuration (e.g., data mixture) for a downstream…
Programming over Thinking: Efficient and Robust Multi-Constraint Planning
Derrick Goh Xin Deik, Quanyu Long, Zhengyuan Liu +2
Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language mod…
The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation
Zhengyuan Liu, Stella Xin Yin, Bryan Chen Zhengyu Tan +5
User simulation is important for developing and evaluating human-centered AI, yet current student simulation in educational applications has significant limitations. Existing appro…
Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD
Bryan Chen Zhengyu Tan, Daniel Wai Kit Chin, Zhengyuan Liu +2
Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable de…
COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content
Zhengyuan Liu, Stella Xin Yin, Dion Hoe-Lian Goh +1
While Generative AI has demonstrated strong potential and versatility in content generation, its application to educational contexts presents several challenges. Models often fail…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…
AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought
Weihua Zheng, Xin Huang, Zhengyuan Liu +7
Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. Although these models show strong reasoning abilities, their pe…
MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders
Wenyu Zhang, Shuo Sun, Bin Wang +6
The rapid advancements in large language models (LLMs) have significantly enhanced natural language processing capabilities, facilitating the development of AudioLLMs that process…
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models
Ayrton San Joaquin, Bin Wang, Zhengyuan Liu +4
Despite advancements, fine-tuning Large Language Models (LLMs) remains costly due to the extensive parameter count and substantial data requirements for model generalization. Acces…
AudioBench: A Universal Benchmark for Audio Large Language Models
Bin Wang, Xunlong Zou, Geyu Lin +6
We introduce AudioBench, a universal benchmark designed to evaluate Audio Large Language Models (AudioLLMs). It encompasses 8 distinct tasks and 26 datasets, among which, 7 are new…
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…
CRAFT: Extracting and Tuning Cultural Instructions from the Wild
Bin Wang, Geyu Lin, Zhengyuan Liu +2
Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications. Despite their wide use cases, their understanding of…
Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions
Zhengyuan Liu, Stella Xin Yin, Carolyn Lee +1
Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in edu…
CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment
Geyu Lin, Bin Wang, Zhengyuan Liu +1
Multilingual proficiency presents a significant challenge for large language models (LLMs). English-centric models are usually suboptimal in other languages, particularly those tha…
Resilience of Large Language Models for Noisy Instructions
Bin Wang, Chengwei Wei, Zhengyuan Liu +2
As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating tex…
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems
Zhengyuan Liu, Stella Xin Yin, Geyu Lin +1
Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience. The emergence of large language models (LLMs) further enables better human-machine…
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Fangkai Jiao, Chengwei Qin, Zhengyuan Liu +2
Large Language Models (LLMs) have demonstrated significant potential in handling complex reasoning tasks through step-by-step rationale generation. However, recent studies have rai…
Exploring Self-supervised Logic-enhanced Training for Large Language Models
Fangkai Jiao, Zhiyang Teng, Bosheng Ding +3
Existing efforts to improve logical reasoning ability of language models have predominantly relied on supervised fine-tuning, hindering generalization to new domains and/or tasks.…