13 citations · 33 across the 26 of their papers we have counts for
28 papers
SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluation
Jiarui Dong, Yin Cai, Zhouhong Gu +7
Large language models (LLMs) have demonstrated strong potential in graphical user interface (GUI) generation, but reliable evaluation remains challenging due to uncontrolled data d…
Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents
Ying He, Zhouhong Gu, Zhecheng Hu +8
Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language…
The "Knowledge-Behavior Gap" in Cultural Taboo Safety of Large Language Models
Ying He, Sihang Jiang, Xingzhou Chen +6
Cultural taboo safety is essential for deploying large language models (LLMs), as culturally insensitive outputs may cause offense or even social harm. However, existing cultural b…
Deep Research as Rubric for Reinforcement Learning
Wangyi Mei, Zhouhong Gu, Zhenhan Bai +9
Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a promising alternative, but ex…
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
Jialing Li, Zhouhong Gu, Yin Cai +1
The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling b…
AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
Zhouhong Gu, Xiaoxuan Zhu, Yin Cai +12
Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains. However, current frameworks face cr…
CompBench: Benchmarking Complex Instruction-guided Image Editing
Bohan Jia, Wenxuan Huang, Yuntian Tang +14
While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack com…
ToReMi: Topic-Aware Data Reweighting for Dynamic Pre-Training Data Selection
Xiaoxuan Zhu, Zhouhong Gu, Baiqian Wu +5
Pre-training large language models (LLMs) necessitates enormous diverse textual corpora, making effective data selection a key challenge for balancing computational resources and m…
LITE: LLM-Impelled efficient Taxonomy Evaluation
Lin Zhang, Zhouhong Gu, Suhang Zheng +4
This paper presents LITE, an LLM-based evaluation method designed for efficient and flexible assessment of taxonomy quality. To address challenges in large-scale taxonomy evaluatio…
RECKON: Large-scale Reference-based Efficient Knowledge Evaluation for Large Language Model
Lin Zhang, Zhouhong Gu, Xiaoran Shi +2
As large language models (LLMs) advance, efficient knowledge evaluation becomes crucial to verifying their capabilities. Traditional methods, relying on benchmarks, face limitation…
GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization
Zhouhong Gu, Xingzhou Chen, Xiaoran Shi +5
Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to…
MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments
Yin Cai, Zhouhong Gu, Zhaohan Du +5
Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly i…
LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection
Yifeng Wang, Zhouhong Gu, Siwei Zhang +5
Explainable fake news detection predicts the authenticity of news items with annotated explanations. Today, Large Language Models (LLMs) are known for their powerful natural langua…
VCEval: Rethinking What is a Good Educational Video and How to Automatically Evaluate It
Xiaoxuan Zhu, Zhouhong Gu, Sihang Jiang +3
Online courses have significantly lowered the barrier to accessing education, yet the varying content quality of these videos poses challenges. In this work, we focus on the task o…
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu +8
Detecting evidence within the context is a key step in the process of reasoning task. Evaluating and enhancing the capabilities of LLMs in evidence detection will strengthen contex…
StrucText-Eval: Evaluating Large Language Model's Reasoning Ability in Structure-Rich Text
Zhouhong Gu, Haoning Ye, Xingzhou Chen +3
The effective utilization of structured data, integral to corporate data strategies, has been challenged by the rise of large language models (LLMs) capable of processing unstructu…
AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior
Zhouhong Gu, Xiaoxuan Zhu, Haoran Guo +10
Language significantly influences the formation and evolution of Human emergent behavior, which is crucial in understanding collective intelligence within human societies. Consider…
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation
Wenhao Huang, Zhouhong Gu, Chenghao Peng +5
Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry ef…
Efficiently Quantifying and Mitigating Ripple Effects in Model Editing
Jianchen Wang, Zhouhong Gu, Xiaoxuan Zhu +5
Large Language Models have revolutionized numerous tasks with their remarkable efficacy. However, editing these models, crucial for rectifying outdated or erroneous information, of…
ConcEPT: Concept-Enhanced Pre-Training for Language Models
Xintao Wang, Zhouhong Gu, Jiaqing Liang +3
Pre-trained language models (PLMs) have been prevailing in state-of-the-art methods for natural language processing, and knowledge-enhanced PLMs are further proposed to promote mod…
Can Large Language Models Understand Real-World Complex Instructions?
Qianyu He, Jie Zeng, Wenhao Huang +14
Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with c…
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Xintao Wang, Qianwen Yang, Yongting Qiu +5
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as complet…
Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models
Zhouhong Gu, Lin Zhang, Jiangjie Chen +8
Detectives frequently engage in information detection and reasoning simultaneously when making decisions across various cases, especially when confronted with a vast amount of info…
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye +16
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive eva…