arxiv preprint - Can Large Language Models Understand Context?

In this episode, we discuss Can Large Language Models Understand Context? by Yilun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng. The paper introduces a novel benchmark consisting of four tasks and nine datasets aimed at rigorously evaluating Large Language Models' (LLMs) ability to understand context. The authors find that while pre-trained dense models show some competency, they are less adept at grasping nuanced contextual information compared to fine-tuned state-of-the-art models. Additionally, the research reveals that applying 3-bit post-training quantization to these models results in decreased performance on the benchmark, with an in-depth analysis provided to explain the findings.