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Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning

Renzhi Wang, Piji Li

TL;DR

The paper investigates why Parameter-Efficient Fine-Tuning (PEFT) struggles to acquire factual knowledge in large language models. By framing knowledge learning in a semantic-distance space, it identifies two key issues: (i) fine-tuning can drift away from target knowledge, and (ii) interference among multiple knowledge items impedes learning. To address this, it introduces a data-filtering strategy and a re-weighted learning objective that incorporate semantic-distance signals, and demonstrates performance gains across multiple open-source LLMs and knowledge datasets. The work provides a semantic foundation for understanding PEFT limitations and offers practical techniques to improve knowledge learning in low-parameter-update regimes. These insights advance PEFT research and open pathways for more robust, knowledge-focused fine-tuning of large language models.

Abstract

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of Large Language Models (LLMs) to various downstream applications. However, the effectiveness of the PEFT diminishes notably when downstream tasks require accurate learning of factual knowledge. In this paper, we adopt a semantic perspective to investigate this phenomenon, uncovering the reasons behind PEFT's limitations in knowledge learning task. Our findings reveal that: (1) PEFT presents a notable risk of pushing the model away from the intended knowledge target; (2) multiple knowledge interfere with each other, and such interference suppresses the learning and expression of knowledge features. Based on these insights, we introduce a data filtering strategy to exclude data that is detrimental to knowledge learning and a re-weighted learning strategy to make the model attentive to semantic distance during knowledge learning. Experimental results demonstrate the effectiveness of the proposed method on open-source large language model, further validate the semantic challenge in PEFT, thus paving the way for future research.

Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning

TL;DR

The paper investigates why Parameter-Efficient Fine-Tuning (PEFT) struggles to acquire factual knowledge in large language models. By framing knowledge learning in a semantic-distance space, it identifies two key issues: (i) fine-tuning can drift away from target knowledge, and (ii) interference among multiple knowledge items impedes learning. To address this, it introduces a data-filtering strategy and a re-weighted learning objective that incorporate semantic-distance signals, and demonstrates performance gains across multiple open-source LLMs and knowledge datasets. The work provides a semantic foundation for understanding PEFT limitations and offers practical techniques to improve knowledge learning in low-parameter-update regimes. These insights advance PEFT research and open pathways for more robust, knowledge-focused fine-tuning of large language models.

Abstract

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of Large Language Models (LLMs) to various downstream applications. However, the effectiveness of the PEFT diminishes notably when downstream tasks require accurate learning of factual knowledge. In this paper, we adopt a semantic perspective to investigate this phenomenon, uncovering the reasons behind PEFT's limitations in knowledge learning task. Our findings reveal that: (1) PEFT presents a notable risk of pushing the model away from the intended knowledge target; (2) multiple knowledge interfere with each other, and such interference suppresses the learning and expression of knowledge features. Based on these insights, we introduce a data filtering strategy to exclude data that is detrimental to knowledge learning and a re-weighted learning strategy to make the model attentive to semantic distance during knowledge learning. Experimental results demonstrate the effectiveness of the proposed method on open-source large language model, further validate the semantic challenge in PEFT, thus paving the way for future research.
Paper Structure (33 sections, 10 equations, 9 figures, 5 tables)

This paper contains 33 sections, 10 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Semantic Perspective Analysis
  3. Knowledge Learning and Semantic Distance
  4. Experimental Settings
  5. Results and Analysis
  6. Single Knowledge
  7. Experiments
  8. Results and Analysis
  9. Multiple Knowledge
  10. Experiments
  11. Results and Analysis
  12. Applications of Our Semantic-Based Understanding
  13. Data Filtering
  14. Method
  15. Experiments
  16. ...and 18 more sections

Figures (9)

  • Figure 1: Illustration of semantic distance.
  • Figure 2: Illustration of (a) the away from target phenomenon in single knowledge and (b) knowledge interference across multiple knowledge in semantic space. Each dot represents original knowledge, with arrows indicating the knowledge learned by the model after training. The central target represents the normalized target knowledge. Green denotes the normal progression towards the target knowledge, red denotes an abnormal deviation away from the target. Dashed lines in (b) enclose multiple knowledge learned simultaneously.
  • Figure 3: The relationship between knowledge learning accuracy and target semantic distance (a) based on model: LLaMA2-7B-chat, (b) based on method: LoRA. Target semantic distance is defined in Equation \ref{['distance']}. Performance degradation occurs under short or long target semantic distance regardless of the method and model.
  • Figure 4: Relationship between deviation phenomenon and semantic distance. Note, on the far left and far right, there is blank space because the proportion there is 0.
  • Figure 5: The PCA projection results of knowledge features at different semantic distances.
  • ...and 4 more figures