4 papers
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…
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 However, editing these models, crucial for rectifying outdated or erroneous information, of…
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…
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…