1 citations · 1 across the 8 of their papers we have counts for
10 papers
TeachBench: A Syllabus-Grounded Framework for Evaluating Teaching Ability in Large Language Models
Zheng Li, Siyao Song, Jingyuan Ma +4
Large language models (LLMs) show promise as teaching assistants, yet their teaching capability remains insufficiently evaluated. Existing benchmarks mainly focus on problem-solvin…
GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
Rang Li, Lei Li, Shuhuai Ren +10
Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language mod…
HauntAttack: When Attack Follows Reasoning as a Shadow
Jingyuan Ma, Rui Li, Zheng Li +4
Emerging Large Reasoning Models (LRMs) consistently excel in mathematical and reasoning tasks, showcasing remarkable capabilities. However, the enhancement of reasoning abilities a…
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
Zheng Li, Qingxiu Dong, Jingyuan Ma +3
Recently, large reasoning models demonstrate exceptional performance on various tasks. However, reasoning models always consume excessive tokens even for simple queries, leading to…
SCoRE: Benchmarking Long-Chain Reasoning in Commonsense Scenarios
Weidong Zhan, Yue Wang, Nan Hu +12
Currently, long-chain reasoning remains a key challenge for large language models (LLMs) because natural texts lack sufficient explicit reasoning data. However, existing benchmarks…
Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming
Rui Li, Peiyi Wang, Jingyuan Ma +3
Large Language Models (LLMs) have gained increasing attention for their remarkable capacity, alongside concerns about safety arising from their potential to produce harmful content…
Plug-and-Play Training Framework for Preference Optimization
Jingyuan Ma, Rui Li, Zheng Li +2
Recently, preference optimization methods such as DPO have significantly enhanced large language models (LLMs) in wide tasks including dialogue and question-answering. However, cur…
Large Language Models Struggle with Unreasonability in Math Problems
Jingyuan Ma, Damai Dai, Zihang Yuan +6
Large Language Models (LLMs) have shown remarkable success on a wide range of math and reasoning benchmarks. However, we observe that they often struggle when faced with unreasonab…
Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu +4
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale…
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
Zhexin Zhang, Yida Lu, Jingyuan Ma +8
The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs'…