Beyond Surface: Probing LLaMA Across Scales and Layers
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.
65727d43379284b904a3190c
Nuo Chen
claimed_verified
2023-12-08T11:21:26.268Z
64f5a9799bd32ae6c82e0bb4
https://cdn-avatars.huggingface.co/v1/production/uploads/64f5a9799bd32ae6c82e0bb4/j7qf4vMJhSLLKqJp5zXig.png
Chen
user
Nuo97
65727d43379284b904a3190d
Ning Wu
65727d43379284b904a3190e
Shining Liang
65727d43379284b904a3190f
Ming Gong
admin_assigned
2023-12-08T10:13:55.533Z
6438ad8f1efe72ba4807e801
/avatars/9f8d63175379fd26881cd2f597a0c17c.svg
Ming Gong
user
Veraaaa
65727d43379284b904a31910
Linjun Shou
65727d43379284b904a31911
Dongmei Zhang
65727d43379284b904a31912
Jia Li
admin_assigned
2023-12-08T10:14:22.234Z
64ad4f24f5d40283c6307c1e
/avatars/0175716395e34d546282d1be4d3a5936.svg
Ji-a Lee
user
jialee0122
Beyond Surface: Probing LLaMA Across Scales and Layers
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.
An analysis of LLaMA focusing on reasoning and computation through multiple-choice tasks reveals layer-specific knowledge and computational capabilities without significant enhancement from larger model sizes.
Large Language Models (LLMs) LLaMA multiple-choice tasks reasoning computation layers knowledge computational prowess hallucinations logical thinking multilingual recognitive abilities