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Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers? (2404.07066v5)

Published 10 Apr 2024 in cs.CL, cs.AI, and cs.LG

Abstract: 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 \url{https://github.com/Luckfort/CD}.

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Authors (13)
  1. Mingyu Jin (38 papers)
  2. Qinkai Yu (10 papers)
  3. Jingyuan Huang (9 papers)
  4. Qingcheng Zeng (30 papers)
  5. Zhenting Wang (41 papers)
  6. Wenyue Hua (51 papers)
  7. Haiyan Zhao (42 papers)
  8. Kai Mei (30 papers)
  9. Yanda Meng (18 papers)
  10. Kaize Ding (59 papers)
  11. Fan Yang (878 papers)
  12. Mengnan Du (90 papers)
  13. Yongfeng Zhang (163 papers)
Citations (18)
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Summary

Exploring Concept Depth in LLMs

Introduction to Concept Depth in LLMs

Recent advancements in LLMs have steered the research community towards understanding how these models encode and process information. Jin et al. delve into this matter by introducing the notion of "Concept Depth" to analyze the knowledge acquisition process across different layers of LLMs. Their paper, Exploring Concept Depth: How LLMs Acquire Knowledge at Different Layers?, presents an empirical paper scrutinizing how varying types of knowledge are encapsulated within different depths of LLMs, ranging from shallow to deep layers. This paper extends the boundaries of conventional model interpretation by partitioning concepts into factual, emotional, and inferential categories and assessing how tasks within these categories are internalized by LLMs at varying conceptual depths.

Probing Technique and Concept Depth Analysis

The research employs a probing technique, derived from linear classifier probes, to investigate layer-wise representations within LLMs. This approach quantifies the level at which specific concepts are best understood by the model, which is indicative of the conceptual knowledge depth captured within that layer. The probing framework developed for this paper not only facilitates a detailed inspection of where and how information is stored across the model's architecture but also illuminates the gradient of concept acquisition from simple to complex within LLMs.

Key Findings on LLMs Learning Capabilities

The paper presents several noteworthy conclusions that elucidate the sophisticated nature of learning embedded within LLMs:

  • Basic Concepts are often grasped at lower conceptual depths, whereas more intricate concepts necessitate a deeper understanding within the model's architecture. This trend remains consistent across various LLM architectures and sizes.
  • A comparative analysis highlights that models with a larger number of parameters generally exhibit superior performance in classifying tasks at earlier layers. This suggests that increasing model size not only enhances its overall capacity for task performance but also potentially shifts the understanding of complex concepts to relatively shallower layers.
  • The paper discusses the robustness of LLMs from a Concept Depth perspective by exploring how additional factors like random noise or quantization impact model performance and concept depth.

Implications and Future Directions

Jin et al.'s exploration into Concept Depth in LLMs provides a novel lens through which the internal workings and knowledge processing mechanisms of these models can be better understood. The implications of this research are manifold, offering new pathways for optimizing model architectures and enhancing computational efficiency without sacrificing performance. Moreover, this work lays the groundwork for future explorations into the interpretability of AI systems, fostering advancements in creating more transparent and understandable AI tools.

Conclusion

In summary, this paper makes a significant contribution to our comprehension of how LLMs encode and process different levels of conceptual information. By introducing and examining Concept Depth, Jin et al. highlight the nuanced manner in which knowledge is distributed across an LLM's layers, offering insights into the intricate relationship between model architecture and learning capabilities. As the AI field continues to evolve, understanding these dynamics will be crucial for both the development of more sophisticated models and the elucidation of their decision-making processes.

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