5 accepted papers
As large language models grow increasingly capable, concerns about their safe deployment have intensified. While numerous alignment strategies aim to restrict harmful behavior, these defenses can still be circumvented through carefully designed adversarial prompts. In this work, we introduce a theor…
Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our…
Even though large language models (LLMs) have demonstrated remarkable capability in solving various natural language tasks, the capability of an LLM to follow human instructions is still an area of active development. Recent works (Ouyang et al., 2022; Rafailov et al., 2023; Zhang et al., 2023) have…
The Transformer architecture has become prominent in developing large causal language models. However, mechanisms to explain its capabilities are not well understood. Focused on the training process, here we establish a meta-learning view of the Transformer architecture when trained for the causal l…
Radiologists’ eye movements during medical image interpretation reflect their perceptual-cognitive behaviour and correlate with diagnostic decisions. Previous study has shown the significance of gaze behaviour of different time intervals for the decision-making process. Being able to automatically p…