# Prompt Tuning for Multi-Label Text Classification: How to Link Exercises to Knowledge Concepts?
## 4 Experiment
### Datasets  
Exercises-Concepts dataset of Data Structure course: To study the problem of linking exercises to knowledge concepts, we construct the Exercises-Concepts dataset of the Data Structure course. Refer to MOOCCube_DS[44] data repository and Several national planning textbooks, we extract 65 classic knowledge concepts. Subsequently, 2027 exercises used in these textbooks are marked with the corresponding knowledge concepts. Details are shown in Table 1.  
Few-shot dataset construction: To simulate the few-shot situation, we reconstruct the dataset in to the form of few-shot learning, where each example is the combination of a query instance \((e^{q},c^{q})\) and the corresponding \(K-shot\) support set \(S\). Unlike the single-label classification problem, instances of multi-label classification may be associated with multiple labels. Therefore, there is no guarantee that each label appears exactly \(K\) times during sampling. To address the problem, we approximately construct \(K-shot\) support set \(S\) with the Minimum-including Algorithm [43]. It constructs a support set generally complying with the following two conditions: (1) All labels in the original dataset appear at least \(K\) times in support set \(S\). (2) At least one label will appear less than K times in \(S\) if any \((e^{q},c^{q})\)pair is removed from it. For the original dataset, we sampled \(N_{S}\) different support sets. For each support set, we take the remaining data as the query set. Each support-query-set pair constitutes one few-shot episode.  
On the test stage, we constructed 10 different few-shot episodes for each selected K-shot. Among them, support set is used to fine tuning model, and query set is used to test the effectiveness of methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(C\) & \(N\) & \(C\) & \(N\) & \(C\) & \(N\) & \(C\) & \(N\) \\ \hline Array & 36 & Sequential List & 46 & Bubble Sort & 41 & Binary Search & 48 \\ \hline Logical Structure & 48 & Linked Storage Structure & 48 & Time Complexity & 91 & Generalized table & 35 \\ \hline Heapsort & 46 & Physical Structure & 35 & Linked List & 10 & Matrix & 35 \\ \hline Adjacency Matrix & 35 & Linear List & 48 & BT-Proorder Traversal & 37 & Tree-Degree & 40 \\ \hline Algorithm & 56 & String & 35 & BT-Postorder Traversal & 35 & Tree-Depth & 45 \\ \hline Queue & 35 & Tree & 24 & Minimum Spanning Tree & 53 & Graph & 18 \\ \hline Revision & 39 & Binary Tree (BT) & 23 & Topological Sort & 45 & Circular Queue & 35 \\ \hline Complete Binary Tree & 42 & Binary Sort Tree & 35 & Depth First Search & 5 & Binary Tree-Thread BinaryTree & 35 \\ \hline Balanced BinaryTree & 50 & Huffman Tree & 35 & Breadth First Search & 48 & Shelf’s Sort & 60 \\ \hline Search & 136 & Data Structure & 35 & Connected Graph & 47 & Binary Tree-Inorder Traversal & 13 \\ \hline Sequential Search & 53 & Sequential Storage Structure & 35 & QuickSort & 68 & Merge Sort & 35 \\ \hline Critical Path & 60 & Stack & 35 & Full Binary Tree & 48 & Space Complexity & 35 \\ \hline Selection Sort & 35 & Strongly Connected Graph & 57 & Graph-Degree & 35 & Selection Sort & 35 \\ \hline HashSearch & 35 & Multi-way Search Tree & 35 & Adjacency List & 37 & Sort & 28 \\ \hline Shortest Path & 35 & Binary Tree-Order Traversal & 56 & Doubly Linked List & 35 & Straight Insertion Sort & 44 \\ \hline Cycle Chain & 14 & Undirected Graph & 27 & Oriented graph & 36 & Data & 12 \\ \hline Double Circle List & 8 & & & & & \\ \end{tabular}
\end{table}
Table 1: The number of each exercise linking to knowledge concepts. \(C\) represents the label of the knowledge concept, \(N\) denotes the number of exercise.