2 citations · 4 across the 5 of their papers we have counts for
5 papers
CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
Bo Peng, Kaiwen Wu, Sirui Chen +3
Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equival…
Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
Yuxuan Qiao, Dongqin Liu, Hongchang Yang +2
LLM agents can combine individually non-revealing tool returns and disclose a sensitive conclusion, creating Tools Orchestration Privacy Risk (TOP-R). We formalize TOP-R through th…
OASIS: Open Agent Social Interaction Simulations with One Million Agents
Ziyi Yang, Zaibin Zhang, Zirui Zheng +20
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agen…
REEF: Representation Encoding Fingerprints for Large Language Models
Jie Zhang, Dongrui Liu, Chen Qian +4
Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefor…
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts
Qibing Ren, Hao Li, Dongrui Liu +7
Safety concerns in large language models (LLMs) have gained significant attention due to their exposure to potentially harmful data during pre-training. In this paper, we identify…