[Paper Review] Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Mingyu Jin, Qinkai Yu+11 more|arXiv (Cornell University)|Apr 10, 2024

Bayesian Modeling and Causal Inference4 citations

TL;DR

This paper introduces 'Concept Depth'—a novel metric to analyze how large language models (LLMs) acquire knowledge across different network layers. By probing layer-wise representations across factual, emotional, and inferential tasks in Gemma, LLaMA, and QWen models, the authors find that simpler concepts emerge in shallow layers, while complex concepts require deeper layers. Robustness analyses show noise and quantization delay conceptual learning to deeper layers.

ABSTRACT

Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities remain poorly understood. In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of "Concept Depth" to suggest that more complex concepts are typically acquired in deeper layers. Specifically, we categorize concepts based on their level of abstraction, defining them in the order of increasing complexity within factual, emotional, and inferential tasks. We conduct extensive probing experiments using layer-wise representations across various LLM families (Gemma, LLaMA, Qwen) on various datasets spanning the three domains of tasks. Our findings reveal that models could efficiently conduct probing for simpler tasks in shallow layers, and more complex tasks typically necessitate deeper layers for accurate understanding. Additionally, we examine how external factors, such as adding noise to the input and quantizing the model weights, might affect layer-wise representations. Our findings suggest that these factors can impede the development of a conceptual understanding of LLMs until deeper layers are explored. We hope that our proposed concept and experimental insights will enhance the understanding of the mechanisms underlying LLMs. Our codes are available at https://github.com/Luckfort/CD.

Motivation & Objective

• To understand how LLMs encode concepts of varying complexity across their network layers.
• To investigate whether simpler or more abstract concepts are processed earlier or later in the model's depth.
• To evaluate the impact of input noise and weight quantization on conceptual learning progression.
• To develop a new metric, 'Concept Depth', to quantify the layer at which different concept types are acquired.
• To provide empirical insights into the representational hierarchy of LLMs across diverse model families and tasks.

Proposed method

• Probing layer-wise hidden representations using linear classifiers to assess performance on factual, emotional, and inferential tasks.
• Categorizing concepts by abstraction level: factual (low), emotional (medium), inferential (high) complexity.
• Training independent linear probes for each layer to determine the optimal performance achievable at that depth.
• Applying input-level noise (random strings) and weight quantization to assess robustness of conceptual learning.
• Evaluating multiple LLM families (Gemma, LLaMA, QWen) across 9 diverse datasets to ensure generalizability.
• Proposing 'Concept Depth' as a metric measuring the layer at which a concept is first reliably detected.

Experimental results

Research questions

• RQ1 Do LLMs process concepts of increasing complexity at progressively deeper layers?
• RQ2 At what layer do factual, emotional, and inferential concepts become detectable in LLM representations?
• RQ3 How does input noise affect the layer at which concepts are acquired in LLMs?
• RQ4 How does model weight quantization influence the depth of conceptual understanding in LLMs?
• RQ5 Is the concept of 'Concept Depth' generalizable across different LLM architectures and task types?

Key findings

• Simpler concepts, such as factual knowledge, are reliably detectable in shallow layers (e.g., layers 1–5) across all evaluated LLM families.
• More complex concepts, such as emotional sentiment and logical inference, require deeper layers (e.g., layers 15–30) for accurate detection.
• Input noise delays the emergence of concept representations, pushing detectable layers deeper in the network.
• Weight quantization similarly shifts the onset of reliable concept detection to deeper layers, indicating reduced early-layer expressivity.
• The 'Concept Depth' metric demonstrates consistent trends across diverse datasets and model families, confirming its generalizability.
• The study confirms that conceptual understanding in LLMs is hierarchical, with increasing abstraction requiring deeper network layers.