8 papers
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…
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…
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…
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…
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…