12 citations · 19 across the 11 of their papers we have counts for
8 papers · 1 filter
OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills
Qiyuan Liu, Tingfeng Hui, Kun Zhan +2
LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra security vulnerabilities, as seem…
Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions
Xuan Yang, Hao Xu, Tingfeng Hui +4
Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world scenarios. Such benchmarks m…
STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics
Tingfeng Hui, Hao Xu, Pengyu Zhu +5
Large language models (LLMs) deployed in real-world agentic applications must be capable of replanning and adapting when mid-task disruptions invalidate their prior decisions. Exis…
Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Qiyuan Liu, Hao Xu, Xuhong Chen +3
Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune LLMs during reinforcement learni…
Generating Fluent Adversarial Examples for Natural Languages
Huangzhao Zhang, Hao Zhou, Ning Miao +1
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make sm…
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
Ning Miao, Yuxuan Song, Hao Zhou +1
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model o…