4 accepted papers
The dueling bandits (DB) problem addresses online learning from relative preferences, where the learner queries pairs of arms and receives binary win-loss feedback. Most existing work focuses on designing algorithms for specific stochastic or adversarial environments. Recently, a unified algorithm h…
Large Language Models (LLMs) excel in natural language processing (NLP) tasks but often generate false or misleading information, known as hallucinations, raising reliability concerns in high-stakes applications. To provide statistical guarantees on the factuality of LLM outputs, conformal predictio…
Large language models (LLMs) are vulnerable to malicious inputs, posing serious risks in high-stakes applications. While existing detection-based methods have shown strong empirical performance, they fail to explicitly control the false acceptance rate (FAR), a critical safety metric in sensitive sc…
Detecting training data for large language models (LLMs) is receiving growing attention, especially in applications requiring high reliability. While numerous efforts have been made to address this issue, they typically focus on accuracy without ensuring controllable results. To fill this gap, we pr…