Recent advances in Artificial Intelligence (AI) have seen widespread applications across do- mains such as computer vision, natural lan- guage processing, and graph-based learning. Among these, Large Language Models (LLMs) have emerged as a cornerstone of modern AI research, with applications ranging from lan- guage understanding to content generation. As research in this field rapidly expands, new- comers face the challenge of navigating an overwhelming number of survey papers that attempt to consolidate knowledge on LLMs. This project addresses this issue by performing a comprehensive data exploration and analysis of metadata from recent LLM survey papers. Through the analysis of publication trends, ci- tation patterns, and topical coverage, we aim to offer insights that simplify the discovery pro- cess for beginners and provide a clearer under- standing of the current landscape. Our findings are intended to facilitate more efficient reading and research in the field of LLMs, enabling new researchers to quickly grasp key developments and emerging trends. 1 Introduction AI techniques have been widely applied to var- ious domains, such as images (He et al., 2016; Dosovitskiy, 2020), texts (Vaswani et al., 2017; De- vlin et al., 2018), and graphs (Kipf and Welling, 2016; Zhuang and Al Hasan, 2022). As a critical subset of AI techniques, Large Language Models (LLMs) have gained significant attention in recent years (Radford et al., 2018, 2019; Brown et al., 2020; Achiam et al., 2023; Bai et al., 2022; Team et al., 2023). Especially, more and more new be- ginners are interested in the research topics about LLMs. To learn the recent progress in this field, new beginners commonly will read survey papers about LLMs. Therefore, to facilitate their learn- ing, numerous survey papers on LLMs have been published in the last two years. However, a largeamount of these survey papers can be overwhelm- ing, making it challenging for new beginners to read them efficiently. To embrace this challenge, in this project, we aim to explore and analyze the metadata of LLMs survey papers, providing insights to enhance their accessibility and under- standing (Zhuang and Kennington, 2024). Specifi- cally, we aim to conduct a comprehensive analysis of the metadata associated with LLM survey pa- pers, focusing on key attributes such as publication trends, author collaborations, and topic evolution over time. By utilizing data exploration techniques, we will identify prevalent themes and gaps in the current literature, which will serve as a guide for newcomers seeking targeted knowledge. Addition- ally, we will visualize the findings through various graphical representations, ensuring that complex information is presented in an intuitive format. Our ultimate goal is to create a resource that simpli- fies the navigation of LLM survey papers, thereby empowering new researchers to engage more ef- fectively with the evolving landscape of AI and LLMs. Overall, our contributions can be summarized as follows: •Conducted data preprocessing steps, includ- ing normalization and encoding of categorical variables, to prepare the dataset for effective analysis and model training. •Implemented techniques to address class im- balance, utilizing methods such as oversam- pling to ensure a more balanced representation of categories in the training dataset. •Employed various machine learning models, including Random Forest classifiers, and eval- uated their performance through metrics such as accuracy, classification reports, and confu- sion matrices. •Analyzed the results of the model evalua- tions to draw insights into classification per- formance, enabling better understanding of the effectiveness of different AI techniques applied to LLM survey papers. 2 Related Work Research on AI techniques has advanced rapidly, with significant applications across various do- mains, including image processing, natural lan- guage processing, and graph analysis. Notable works include those by He et al. (2016) and Doso- vitskiy (2020) in image classification, as well as Vaswani et al. (2017) and Devlin et al. (2018), who pioneered transformer architectures that have become foundational for modern LLMs. Within the domain of large language models, sev- eral studies have explored their capabilities and be- haviors. For instance, Radford et al. (2018, 2019) and Brown et al. (2020) highlighted the advance- ments in LLMs, demonstrating their potential for generating coherent and contextually relevant text. In recent years, the exploration of ethical consider- ations and the societal impact of LLMs has gained traction, as discussed by Bai et al. (2022) and Achiam et al. (2023). Survey papers play a crucial role in synthesizing existing research, offering newcomers a compre- hensive overview of developments in LLMs. How- ever, the proliferation of these surveys, as noted by Zhuang and Al Hasan (2022), can lead to infor- mation overload for readers attempting to navigate the field. Therefore, it is essential to streamline the information presented in these papers to enhance accessibility and facilitate effective learning. In this project, we aim to build upon the existing literature by analyzing the metadata of LLM survey papers, identifying trends, and offering insights that will aid in improving the comprehension and utility of these resources for researchers and practitioners alike. 3 Methodology 3.1 Data Exploration In this section, we conduct a comprehensive explo- ration of the survey papers dataset. Our primary objectives include analyzing the trends in the re- lease of survey papers over time and investigating the distribution of proposed taxonomies. 3.2 Trends of Survey Papers Over Time To visualize the trends in the number of survey papers released over time, we plot the aggregated data, which shows the count of survey papers permonth. This visualization helps in understanding how the interest in survey papers has evolved over the months. As shown in Figure 1, the trend indicates notable fluctuations in the number of publications, with peaks suggesting periods of heightened interest or activity in specific areas of research. Analyzing these trends can provide insights into when signif- icant advancements or discussions around Large Language Models (LLMs) occurred, reflecting the dynamics of the research community. 2021-07 2022-08 2022-12 2023-02 2023-03 2023-04 2023-05 2023-06 2023-07 2023-08 2023-09 2023-10 2023-11 2023-12 2024-01 Month-Year0.02.55.07.510.012.515.017.520.0Number of Papers Number of Survey Papers Released Over Time Figure 1: Trend of Survey Papers Released Over Time To further analyze the dataset, we calculate the mean number of surveys released per month, along with other descriptive statistics. This provides in- sights into the average publication rate and the dis- tribution of papers over the time period studied. 3.3 Distribution of Proposed Taxonomy Next, we examine the distribution of the proposed taxonomies within the dataset. By counting the occurrences of each taxonomy, we can visualize this distribution as shown in Figure 2. This analysis helps us understand the variety of topics covered in the survey papers and highlights areas with higher research activity. Trustworthy ComprehensivePromptingScience RecSys & IR Multi-modal & Pre-trainingEvaluation Software EngineeringAdaptation TuningRoboticsGraphs OthersLaw Finance Education Hardware Architecture T axonomy0510152025Number of PapersDistribution of Proposed T axonomy Figure 2: Distribution of Proposed Taxonomy Through this exploration, we gain valuable in- sights into the publication trends of survey papers and the diversity of proposed taxonomies, contribut- ing to a better understanding of the current land- scape in the field of AI-generated content. 3.4 Analysis of Papers by Taxonomy In addition to the previously discussed trends, we conducted a comprehensive analysis of the papers based on their taxonomy. This analysis provides insights into the distribution and characteristics of the survey papers included in our dataset. 3.4.1 Statistical Analysis To better understand the distribution of papers across different taxonomies, we calculated several statistical metrics, including the median, mode, and quantiles. - The **median** number of papers per taxon- omy was found to be 8.5. - The **mode**, which indicates the most frequently occurring taxonomy, was1. - We also computed the quantiles (25th, 50th, and 75th percentiles) of paper counts by taxonomy, providing a clearer picture of the distribution. The quantiles are as follows: - 25th Percentile: 4.25 - 50th Percentile: 8.50 - 75th Percentile: 10.75 Furthermore, we sorted the taxonomy counts to visualize the distribution clearly, as shown in Table 1. Taxonomy Count Finance 1 Education 1 Hardware Architecture 1 Law 2 Others 5 Adaptation Tuning 8 Robotics 8 Graphs 8 Multi-modal & Pre-training 9 Evaluation 9 Software Engineering 9 RecSys & IR 10 Science 13 Comprehensive 17 Prompting 17 Trustworthy 26 Table 1: Sorted Counts of Papers by Taxonomy 3.4.2 Cumulative Distribution To further illustrate the distribution of papers by taxonomy, we calculated and plotted the cumula- tive distribution, as shown in Figure 3. The cumu- lative distribution provides insights into how thetotal number of papers accumulates across different taxonomies. Adaptation TuningEducationFinance Hardware Architecture Multi-modal & Pre-trainingPromptingRobotics Software Engineering T axonomy20406080100120140Cumulative Number of Papers Cumulative Distribution of Papers by T axonomy Figure 3: Cumulative Distribution of Papers by Taxon- omy As seen in the figure, the cumulative number of papers increases steadily, indicating that some taxonomies have significantly more papers than others. This visualization helps identify which ar- eas of research are more prominent in the survey papers. 3.4.3 Distribution Visualization Additionally, we created a pie chart to visualize the proportion of papers by taxonomy, as illustrated in Figure 4. This representation helps in quickly as- sessing the relative contributions of each taxonomy to the overall dataset. Trustworthy 18.1% Comprehensive11.8% Prompting11.8% Science9.0%RecSys & IR6.9%Multi-modal & Pre-training 6.2%Evaluation 6.2%Software Engineering 6.2%Adaptation Tuning 5.6%Robotics 5.6%Graphs 5.6%Others 3.5%Law 1.4%Finance 0.7%Education 0.7%Hardware Architecture 0.7%Distribution of Papers by T axonomy Figure 4: Distribution of Papers by Taxonomy The pie chart reveals the dominance of specific taxonomies in the dataset, highlighting areas where research interest is particularly high. 3.4.4 Correlation Analysis We calculated the correlation matrix to examine relationships among the variables in our dataset. The correlation matrix is displayed in Figure 5. This heatmap provides a visual representation of the correlations among the numeric variables. num_authors category_code release_date_numnum_authors category_code release_date_num1.00 -0.09 -0.03 -0.09 1.00 0.12 -0.03 0.12 1.00Correlation Matrix 0.00.20.40.60.81.0 Figure 5: Correlation Matrix Heatmap The correlation analysis reveals minimal linear relationships among the variables, indicating that other factors may influence these relationships. 3.5 Data Manipulation To prepare the dataset for modeling, we constructed a feature matrix from the data. This involved sev- eral steps: 1. **Feature Extraction:** We utilized the TF-IDF (Term Frequency-Inverse Document Fre- quency) vectorizer to transform the ‘Title‘ and ‘Summary‘ columns into numerical representations. Additionally, we applied one-hot encoding to the ‘Categories‘ column to represent categorical infor- mation. 2. **Normalization:** The resulting feature ma- trix was normalized using the ‘StandardScaler‘ to ensure that all features contribute equally to the analysis. 3. **Label Encoding:** The labels for the clas- sification task were encoded using ‘LabelEncoder‘ to convert categorical labels into numeric format. 4. **Dataset Splitting:** Finally, we split the dataset into training and testing sets, with a test ratio of 0.4. The dimensions of the resulting datasets are as follows: - Training feature matrix shape: (86 ,3547) - Testing feature matrix shape: (58 ,3547) - Training labels shape: (86 ,)- Testing labels shape: (58 ,) This preprocessing ensures that the dataset is well-structured for subsequent analysis and model- ing tasks.3.6 Data Evaluation To analyze the datasets, we employed a Random Forest classifier, a robust machine learning model known for its effectiveness in classification tasks. 1. **Model Training:** We trained the Random Forest classifier using the training dataset. The model was initialized with a random state for re- producibility. 2. **Predictions:** After training, we made predictions on the test set. 3. **Performance Evaluation:** The model’s performance was evaluated using accuracy as the primary metric. Additionally, we calculated a confusion matrix and a classification report to gain deeper insights into the model’s performance across different categories. The evaluation results are as follows: - **Accu- racy:** 0.397- **Classification Report:** precision recall f1-score support 0 0.35 0.50 0.41 20 1 0.20 0.14 0.17 14 2 0.33 0.18 0.23 11 3 0.50 0.33 0.40 12 4 0.54 0.73 0.62 15 5 0.40 0.25 0.31 12 6 0.62 0.77 0.69 13 7 0.71 0.29 0.41 14 8 0.00 0.00 0.00 0 9 0.50 0.40 0.44 10 accuracy 0.40 58 macro avg 0.41 0.39 0.38 58 weighted avg 0.44 0.40 0.39 58 The accuracy score indicates that the model has moderate performance, suggesting room for im- provement. Further tuning of hyperparameters or exploring different models may enhance classifica- tion performance. 3.7 Further Evaluation To address the class imbalance in our dataset, we applied the Random Oversampling technique from theimblearn library. This involved initializing the RandomOverSampler and using it to resample the training data, thereby balancing the class distribu- tion. After resampling, we confirmed that each class had an equal number of instances. Following this, we trained a Random Forest clas- sifier on the resampled training set. The classifier was fitted to the newly balanced data, and predic- tions were made on the test set. The accuracy score achieved after implement- ing the oversampling technique was approximately 0.55. This indicates a moderate improvement in model performance, suggesting that the oversam- pling helped the Random Forest classifier make better predictions. However, there remains poten- tial for further enhancement. Additional hyperpa- rameter tuning or exploring alternative models may provide even better classification performance. 3.7.1 Confusion Matrix Visualization To further evaluate the model’s performance, we created a confusion matrix, which provides a de- tailed breakdown of the classifier’s predictions ver- sus the actual labels. The confusion matrix was generated using the confusion_matrix function from the sklearn.metrics module. A heatmap representation of the confusion ma- trix is shown in Figure 6. The heatmap visualizes the counts of true positive, true negative, false posi- tive, and false negative predictions, allowing us to quickly assess which classes are being confused by the classifier. The matrix is organized such that: • The rows correspond to the true labels, and •The columns correspond to the predicted la- bels. This visualization aids in identifying specific classes where the model may be underperforming or making systematic errors. The plot was saved in a PDF format for clarity and ease of interpretation. 0123456789101112131415161718192021222324 Predicted Label0123456789101112131415161718192021222324True Label00005000000000000000000000 01000000000000000000000000 01000000000000000000000000 01000000000000000000000000 000012 000000000000000000000 0000011 00000000000000000000 00000000010000000000000000 00000100000000000000000000 00000100000000000000000000 00000100030000000000000000 00001000000000000000000000 00002100000000000000000000 00000100000000000000000000 00000000000000000010000000 00000100000000000000000000 00001000000000000000000000 00001000000000000000000000 00000000000000000010000000 00000000000000000000000000 00001000000000000000000000 00000100000000000000000000 00000100000000000000000000 00000000000000000000002000 00000100000000000000000000 00000000000000000000001000 00000000000000000000000003Confusion Matrix Figure 6: Confusion Matrix for Class Imbalance4 Conclusion In this study, we conducted a comprehensive anal- ysis of the dataset, focusing on the relationships among various features and the distribution of cate- gories. Our correlation analysis highlighted mini- mal linear relationships, suggesting that more com- plex interactions might be at play. To enhance model performance, we implemented a feature ma- trix combining textual data and categorical vari- ables, followed by preprocessing to normalize and encode the data effectively. To address class imbalance, we employed Ran- dom Oversampling, which successfully balanced the training set, ensuring an equal number of in- stances across all categories. Subsequently, we trained a Random Forest classifier, achieving an accuracy score of approximately 55.2%. This per- formance indicates a moderate level of classifica- tion accuracy, revealing potential areas for further improvement. Future work may involve hyperpa- rameter tuning, exploring alternative models, and employing additional techniques to boost predic- tive performance. Overall, this analysis lays a strong foundation for future investigations, providing insights into the dataset’s structure and guiding subsequent model- ing efforts. A APPENDIX This appendix provides additional details on the settings and hyperparameters used in our analysis, as well as other relevant information. A.1 Model Hyperparameters The following hyperparameters were used for the Random Forest classifier: •Number of Estimators: 100 •Max Depth: None (nodes are expanded until all leaves are pure) •Min Samples Split: 2 •Min Samples Leaf: 1 •Random State: 42 A.2 Data Preprocessing Settings The feature matrix was constructed using the fol- lowing methods: •TF-IDF Vectorization: – Stop Words: English •One-Hot Encoding: Applied to the ’Cate- gories’ column. A.3 Resampling Technique To address class imbalance in the training dataset, we utilized the Random Oversampling technique. This ensured that each class had an equal number of instances, as indicated below: Resampled training labels distribution: 7 17 25 17 2 17 31 17 17 17 43 17 6 17 36 17 18 17 14 17 26 17 39 17 29 17 33 17 20 17 4 17 41 17 28 17 32 17 0 17 37 17 42 17 15 17 40 17 1 17 13 17 3 17 27 17 9 17 46 17 5 17 8 17 19 17 A.4 Software and Libraries Used The analysis was conducted using the following libraries and frameworks: •Pandas for data manipulation. •Scikit-learn for model training and evalua- tion.•Imbalanced-learn for resampling techniques. •Matplotlib andSeaborn for data visualiza- tion.