Summary of Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?, by Mingyu Jin et al.
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?
by Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how large language models (LLMs) process tasks of varying complexities. The authors propose the concept of “Concept Depth,” suggesting that LLMs acquire more complex concepts in deeper layers. They categorize concepts by level of abstraction and conduct probing experiments on various LLM families (Gemma, LLaMA, Qwen) across different datasets. The results show that simpler tasks are processed in shallow layers, while more complex tasks require deeper layers for accurate understanding. The authors also examine how external factors like noise or quantized model weights affect layer-wise representations, finding that these factors can impede the development of a conceptual understanding of LLMs until deeper layers are explored. Overall, this study aims to enhance our understanding of the mechanisms underlying LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how large language models work on different types of tasks. The researchers found that these models can understand simpler things quickly, but more complex ideas require more time and effort. They tested the models on many tasks and showed that they can’t figure out the harder ones until they look deeper inside the model. The authors also wanted to see how adding noise or changing the way the model works affects its understanding. Overall, this study helps us understand how language models work and what makes them good at certain things. |
Keywords
* Artificial intelligence * Llama
