When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols.

For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).

Interpretability work in machine learning (ML) seeks to develop tools that make models more intelligible to humans in order to better monitor model behavior and predict failure modes. Early work in interpretability sought to identify relationships between model outputs and input features (Ribeiro et al., 2016; Covert et al., 2022), but with only black-box query access to observe inputs and outputs, it can be difficult to evaluate a model's internal logic.

Imitation learning (IL) aims to recover the expert behavior from the dataset of demonstrations.

Hence, the following research questions arise: What is the optimal regret lower bound in contextual MNL bandits?

Despite the remarkable progress, they are vulnerable to deliberate attacks, giving rise to concerns about the reliability and trustworthiness of these models in real-world scenarios.

Lagrangian approach, which involves adding a regularization term to the loss function.

Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs).