192 citations · 424 across the 155 of their papers we have counts for
15 papers · 1 filter
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
Yilin Jiang, Xiaorong Zhu, Fei Tan +9
Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators. These roles place demands that ordinary question answering does…
UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities
Qi Jia, Haodong Zhao, Dun Pei +7
Benchmarking large language models (LLMs) and agents in multi-turn interactive scenarios is essential for understanding their practical capabilities. However, existing evaluation p…
Automated Safety Benchmarking: A Multi-agent Pipeline for LVLMs
Xiangyang Zhu, Yuan Tian, Zicheng Zhang +6
Large vision-language models (LVLMs) exhibit remarkable capabilities in cross-modal tasks but face significant safety challenges, which undermine their reliability in real-world ap…
EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory
Ye Shen, Dun Pei, Yiqiu Guo +6
Despite recent advances in understanding and leveraging long-range conversational memory, existing benchmarks still lack systematic evaluation of large language models(LLMs) across…
Q-Mirror: Unlocking the Multi-Modal Potential of Scientific Text-Only QA Pairs
Junying Wang, Zicheng Zhang, Ye Shen +8
High-quality, multi-modal benchmarks are crucial for advancing scientific reasoning in large models yet their manual creation is costly and unscalable. To address this bottleneck,…
A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation
Ye Shen, Junying Wang, Farong Wen +4
The rapid progress of Multi-Modal Large Language Models (MLLMs) has spurred the creation of numerous benchmarks. However, conventional full-coverage Question-Answering evaluations…
QoNext: Towards Next-generation QoE for Foundation Models
Yijin Guo, Farong Wen, Ye Shen +5
Existing evaluations of foundation models predominantly focus on output correctness, treating interaction as a static exchange of information. However, such perspectives overlook t…
User-centric Subjective Leaderboard by Customizable Reward Modeling
Qi Jia, Xiujie Song, Zicheng Zhang +4
Existing benchmarks for large language models (LLMs) predominantely focus on assessing their capabilities through verifiable tasks. Such objective and static benchmarks offer limit…
Affordance Benchmark for MLLMs
Junying Wang, Wenzhe Li, Yalun Wu +6
Affordance theory suggests that environments inherently provide action possibilities shaping perception and behavior. While Multimodal Large Language Models (MLLMs) achieve strong…
The Ever-Evolving Science Exam
Junying Wang, Zicheng Zhang, Yijin Guo +9
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progr…
Research-Oriented Human-Centric Evaluation for Foundation Models
Yijin Guo, Kaiyuan Ji, Xiaorong Zhu +5
Most current evaluations of foundation models focus on objective benchmarks, such as knowledge coverage and reasoning accuracy, often overlooking users' subjective experiences in h…
Improve MLLM Benchmark Efficiency through Interview
Farong Wen, Yijin Guo, Junying Wang +6
The rapid development of Multimodal Large Language Models (MLLM) has led to a wide range of MLLM applications, and a number of benchmark datasets have sprung up in order to assess…
Information Density Principle for MLLM Benchmarks
Chunyi Li, Xiaozhe Li, Zicheng Zhang +8
With the emergence of Multimodal Large Language Models (MLLMs), hundreds of benchmarks have been developed to ensure the reliability of MLLMs in downstream tasks. However, the eval…
Redundancy Principles for MLLMs Benchmarks
Zicheng Zhang, Xiangyu Zhao, Xinyu Fang +6
With the rapid iteration of Multi-modality Large Language Models (MLLMs) and the evolving demands of the field, the number of benchmarks produced annually has surged into the hundr…
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis
Yingjie Zhou, Zicheng Zhang, Jiezhang Cao +6
Artificial Intelligence (AI) has demonstrated significant capabilities in various fields, and in areas such as human-computer interaction (HCI), embodied intelligence, and the desi…