Optimizing Language Model's Reasoning Abilities with Weak Supervision
Optimizing Language Model's Reasoning Abilities with Weak Supervision
While Large Language Models (LLMs) have demonstrated proficiency in handling complex queries, much of the past work has depended on extensively annotated datasets by human experts. However, this reliance on fully-supervised annotations poses scalability challenges, particularly as models and data requirements grow. To mitigate this, we explore the potential of …