8 papers
Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models
Wei Liu, Peijie Yu, Michele Orini +2
The agency expected of Agentic Large Language Models goes beyond answering correctly, requiring autonomy to set goals and decide what to explore. We term this investigatory intelli…
Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies
Wenjin Hou, Wei Liu, Han Hu +3
Multimodal Large Language Models (MLLMs) have shown remarkable proficiency on general-purpose vision-language benchmarks, reaching or even exceeding human-level performance. Howeve…
Are We Evaluating the Edit Locality of LLM Model Editing Properly?
Wei Liu, Haomei Xu, Hongkai Liu +5
Model editing has recently emerged as a popular paradigm for efficiently updating knowledge in LLMs. A central desideratum of updating knowledge is to balance editing efficacy, i.e…
RM-Distiller: Exploiting Generative LLM for Reward Model Distillation
Hongli Zhou, Hui Huang, Wei Liu +8
Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. Due to the difficulty of obtaining high-quality human preference annotation…
SecReEvalBench: A Multi-turned Security Resilience Evaluation Benchmark for Large Language Models
Huining Cui, Wei Liu
The increasing deployment of large language models in security-sensitive domains necessitates rigorous evaluation of their resilience against adversarial prompt-based attacks. Whil…
UQ: Assessing Language Models on Unsolved Questions
Fan Nie, Ken Ziyu Liu, Zihao Wang +11
Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usa…