Summary of Frontier Models Are Capable Of In-context Scheming, by Alexander Meinke et al.
Frontier Models are Capable of In-context Schemingby Alexander Meinke, Bronson Schoen, Jérémy Scheurer, Mikita Balesni,…
Frontier Models are Capable of In-context Schemingby Alexander Meinke, Bronson Schoen, Jérémy Scheurer, Mikita Balesni,…
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrationsby Anian Ruoss, Fabio Pardo,…
On Limitations of LLM as Annotator for Low Resource Languagesby Suramya Jadhav, Abhay Shanbhag, Amogh…
HourVideo: 1-Hour Video-Language Understandingby Keshigeyan Chandrasegaran, Agrim Gupta, Lea M. Hadzic, Taran Kota, Jimming He,…
UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Modelsby Sejoon Oh,…
BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacksby Yunhan Zhao, Xiang Zheng, Lin…
Sequential Large Language Model-Based Hyper-parameter Optimizationby Kanan Mahammadli, Seyda ErtekinFirst submitted to arxiv on: 27…
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Modelsby Fengchen Liu, Jordan…
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Modelsby Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran,…
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Modelsby Fei Wang,…