159 citations · 387 across the 84 of their papers we have counts for
6 papers · 1 filter
IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs
Songlin Bai, Xintong Wang, Linlin Yu +12
In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regula…
ASTRA-bench: Evaluating Tool-Use Agent Reasoning and Action Planning with Personal User Context
Zidi Xiu, David Q. Sun, Kevin Cheng +9
Next-generation AI must manage vast personal data, diverse tools, and multi-step reasoning, yet most benchmarks remain context-free and single-turn. We present ASTRA-bench (Assista…
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Guoli Yin, Haoping Bai, Shuang Ma +21
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, whi…
Automatic and Universal Prompt Injection Attacks against Large Language Models
Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang +2
Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. However, their capabilities can be ex…
How Far Are We from Intelligent Visual Deductive Reasoning?
Yizhe Zhang, He Bai, Ruixiang Zhang +4
Vision-Language Models (VLMs) have recently demonstrated incredible strides on diverse vision language tasks. We dig into vision-based deductive reasoning, a more sophisticated but…
KGLens: Towards Efficient and Effective Knowledge Probing of Large Language Models with Knowledge Graphs
Shangshang Zheng, He Bai, Yizhe Zhang +3
Large Language Models (LLMs) might hallucinate facts, while curated Knowledge Graph (KGs) are typically factually reliable especially with domain-specific knowledge. Measuring the…