2 citations · 4 across the 12 of their papers we have counts for
14 papers
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
Zirui Wu, Lin Zheng, Jiacheng Ye +5
Block diffusion language models accelerate decoding through parallel block-wise denoising, yet whether they can be reliably scaled for long chain-of-thought (CoT) reasoning remains…
From Single to Societal: Analyzing Persona-Induced Bias in Multi-Agent Interactions
Jiayi Li, Xiao Liu, Yansong Feng
Large Language Model (LLM)-based multi-agent systems are increasingly used to simulate human interactions and solve collaborative tasks. A common practice is to assign agents with…
CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts
Jiuheng Lin, Cong Jiang, Zirui Wu +2
Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs). However, standard outcome-based reinforcement learning (RL) on MCQ…
Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books
Chen Zhang, Jiuheng Lin, Xiao Liu +2
While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains…
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon
Chen Zhang, Zhiyuan Liao, Yansong Feng
Despite substantial research efforts evaluating how well large language models~(LLMs) handle global cultural diversity, the mechanisms behind their cultural knowledge acquisition,…
RefTool: Reference-Guided Tool Creation for Knowledge-Intensive Reasoning
Xiao Liu, Da Yin, Zirui Wu +1
Large Language Models (LLMs) can enhance their reasoning capabilities by using external tools. However, many tasks lack predefined tools. Prior works have explored instructing LLMs…
Augmenting Research Ideation with Data: An Empirical Investigation in Social Science
Xiao Liu, Xinyi Dong, Xinyang Gao +2
Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectivene…
Haste Makes Waste: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between Actions
Zirui Wu, Xiao Liu, Jiayi Li +2
While Large Language Model-based agents have demonstrated substantial progress in task completion, existing evaluation benchmarks tend to overemphasize single-task performance, wit…
Automated Annotation of Evolving Corpora for Augmenting Longitudinal Network Data: A Framework Integrating Large Language Models and Expert Knowledge
Xiao Liu, Zirui Wu, Jiayi Li +3
Longitudinal network data are essential for analyzing political, economic, and social systems and processes. In political science, these datasets are often generated through human…
DiNeR: a Large Realistic Dataset for Evaluating Compositional Generalization
Chengang Hu, Xiao Liu, Yansong Feng
Most of the existing compositional generalization datasets are synthetically-generated, resulting in a lack of natural language variation. While there have been recent attempts to…
ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context
Zirui Wu, Yansong Feng
Tables play a crucial role in conveying information in various domains. We propose a Plan-then-Reason framework to answer different types of user queries over tables with sentence…
Teaching Large Language Models an Unseen Language on the Fly
Chen Zhang, Xiao Liu, Jiuheng Lin +1
Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available…
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data
Xiao Liu, Zirui Wu, Xueqing Wu +3
Quantitative reasoning is a critical skill to analyze data, yet the assessment of such ability remains limited. To address this gap, we introduce the Quantitative Reasoning with Da…
CASA: Causality-driven Argument Sufficiency Assessment
Xiao Liu, Yansong Feng, Kai-Wei Chang
The argument sufficiency assessment task aims to determine if the premises of a given argument support its conclusion. To tackle this task, existing works often train a classifier…