3 accepted papers
Selecting a pretrained large language model (LLM) to fine-tune for a task-specific dataset can be time-consuming and costly. With several candidate models available to choose from, varying in size, architecture, and pretraining data, finding the best model for a specific task often involves extensiv…
Intelligent agents in real-world applications must adapt their behavior to changing contexts and user preferences. For example, planning a road trip requires considering both travel time and cost. Multi-objective reinforcement learning (MORL) provides a principled approach to navigate such trade-of
Fine-tuning pretrained language models on task-specific data is a common practice in Natural Language Processing (NLP) applications. However, the number of pretrained models available to choose from can be very large, and it remains unclear how to select the optimal model without spending considerab…