Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?

This repository presents the implementation of our work. We introduce Concept Depth🔍 to measure where a concept is learned by LLMs with different sizes.

Paper Link: https://arxiv.org/abs/2404.07066

This novel concept is used to analyze the comprehension ability of Large Language Models (LLM) and the difficulty of a concept's understanding. We use probing techniques🔍 on each layer's embedding to detect the layer accuracy, F1-score, and AUC of the classification task.

Key Discoveries💡

• LLMs tend to effectively categorize simple (low-level) tasks, indicating that these concepts are learned in the first few initial layers.

• Difficult (High-level) tasks may only be recognizable (if at all) in deeper layers, and LLMs of the same size perform largely consistently across datasets in terms of concept depth.

Requirements✨

pip install -r requirements.txt

Dataset📕

We apply nine datasets to evaluate Concept Depth.

Raw Files

See: ./dataset

Dataset Size

Here shows the data samples we use to train the LLMs in each dataset.

Dataset	Number of Samples
StrategyQA	2290
Coinflip	500
Cities	1496
CommonClaim	6000
Counterfact	4000
HateEval	6000
STSA	6920
IMDb	2000
Sarcasm	6000

Run the Code💻

Note: if your huggingface token is not ready, please try the code below:

from huggingface_hub import notebook_login
notebook_login()

Run by Jupyter Please open run.ipynb for jupyter environment.

Run by One-click

python run.py

Run by Instructions

python3 main.py --model $MODEL --quant QUANT --noise NOISE --dataset $DATASET

$MODEL: Model name.

$DATASET: Dataset name.

QUANT: The number of bits for quantization. Could be 8, 16, and 32.

NOISE: Replace NOISE with "non-noise" or "noise". "non-noise" for no noise perturbation. "noise" for noise perturbation. Please remove the quotation marks when replacing.

Feel free to change these 4 variables. For example: If we want to train Gemma-2B over dataset STSA, the instruction is:

python3 main.py --model google/gemma-2b --dataset STSA

If we want to train Gemma-2B over dataset STSA, using 16 bit quantization, the instruction is:

python3 main.py --model google/gemma-2b --dataset STSA --quant 16 

If we want to train Gemma-2B over dataset STSA with noise perturbation, the instruction is:

python3 main.py --model google/gemma-2b --dataset STSA --noise noise

Other cases could be found here.

Model name	$MODEL
Gemma-2B	google/gemma-2b
Gemma-7B	google/gemma-7b
LLaMA-7B	meta-llama/Llama-2-7b-chat-hf
LLaMA-13B	meta-llama/Llama-2-13b-chat-hf
QWen-0.5B	Qwen/Qwen1.5-0.5B
QWen-1.8B	Qwen/Qwen1.5-1.8B
QWen-4B	Qwen/Qwen1.5-4B
QWen-7B	Qwen/Qwen1.5-7B
QWen-14B	Qwen/Qwen1.5-14B

Dataset	$DATASET
StrategyQA	StrategyQA
Coinflip	coinflip
Cities	cities
CommonClaim	common
Counterfact	counterfact
HateEval	hateeval
STSA	STSA
IMDb	IMDb
Sarcasm	sarcasm

Citation

Please cite the following paper corresponding to the repository:

@article{jin2024exploring,
  title={Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?},
  author={Jin, Mingyu and Yu, Qinkai and Huang, Jingyuan and Zeng, Qingcheng and Wang, Zhenting and Hua, Wenyue and Zhao, Haiyan and Mei, Kai and Meng, Yanda and Ding, Kaize and others},
  journal={arXiv preprint arXiv:2404.07066},
  year={2024}
}