Beyond Surface: Probing LLaMA Across Scales and Layers
Authors: name: Nuo Chen, name: Ning Wu, name: Shining Liang, name: Ming Gong, name: Linjun Shou, name: Dongmei Zhang, name: Jia Li

This paper presents an in-depth analysis of Large Language Models (LLMs),
focusing on LLaMA, a prominent open-source foundational model in natural
language processing. Instead of assessing LLaMA through its generative output,
we design multiple-choice tasks to probe its intrinsic understanding in
high-order tasks such as reasoning and computation. We examine the model
horizontally, comparing different sizes, and vertically, assessing different
layers. We unveil several key and uncommon findings based on the designed
probing tasks: (1) Horizontally, enlarging model sizes almost could not
automatically impart additional knowledge or computational prowess. Instead, it
can enhance reasoning abilities, especially in math problem solving, and helps
reduce hallucinations, but only beyond certain size thresholds; (2) In vertical
analysis, the lower layers of LLaMA lack substantial arithmetic and factual
knowledge, showcasing logical thinking, multilingual and recognitive abilities,
with top layers housing most computational power and real-world knowledge.