As Large Language Models (LLMs) have be- come more popular in recent years, the number of survey papers in this field has greatly in- creased. This large amount of research can be difficult for new researchers to navigate. In this paper, I analyze the metadata of these LLM sur- vey papers, looking at publication trends and grouping them by research focus areas. I build a feature matrix using simple methods like TF- IDF vectorization and one-hot encoding. I also use a logistic regression model to predict the category of each paper based on its metadata. To address class imbalance in the data, I ap- ply class weighting to improve performance for less common categories. The results show an improvement in accuracy, from 43% to 47%, showing that class weighting helps. This pa- per provides insights into research trends in the LLM field and helps new researchers explore the literature and find areas for future work. 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, Devlin 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 large amount 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, I aim to explore and analyze themetadata of LLMs survey papers, providing in- sights to enhance their accessibility and understand- ing [Zhuang and Kennington, 2024]. Specifically, I aim to systematically analyze the metadata of these LLM survey papers by examining publication trends, categorizing research topics, and identify- ing gaps in the existing literature. Overall, my contributions can be summarized as follows: •I conducted a thorough analysis of the meta- data from LLM survey papers, identifying key trends in publication dates and categorizing research focus areas. •I built a feature matrix by applying Term Frequency-Inverse Document Frequency (TF- IDF) vectorization to the textual data and one- hot encoding to categorical data, enabling ma- chine learning analysis. •I implemented a logistic regression model to classify survey papers based on their metadata, addressing class imbalance through class- weighted techniques. •I evaluated the performance of the logistic regression model using metrics such as ac- curacy, precision, recall, and F1-score, and visualized classification performance through a confusion matrix. •My analysis provides insights into the distri- bution of research areas within LLM-related studies, helping newcomers navigate the liter- ature more effectively and identify underex- plored topics for future research. 2 Related Work Large Language Models (LLMs) have seen rapid advancements in recent years. GPT-4 by [Achiam et al., 2023] exemplifies this progress, showcas- ing improvements in text generation and handling complex tasks. [Bai et al., 2022] introduced Con- stitutional AI, focusing on making AI systems safer through human feedback. Language models like GPT-3, as explored by [Brown et al., 2020], rev- olutionized few-shot learning, enabling models to generalize from minimal data. The introduction of BERT by [Devlin et al., 2018] has also become foundational for many NLP tasks. While these papers highlight advancements in LLM development, relatively few studies have fo- cused specifically on categorizing or analyzing sur- vey papers within the LLM field. Survey papers typically provide overviews of advancements in LLMs, addressing technical challenges, ethical con- cerns, and application domains. However, research explicitly aiming to systematically categorize and analyze the landscape of LLM-related surveys is scarce. In contrast to existing works that focus on techni- cal contributions or ethical frameworks, this paper addresses the lack of structured analysis of LLM survey papers. Existing reviews tend to focus on the functionality and performance of specific mod- els, while this work provides a meta-analysis of the survey literature itself. By focusing on publication trends, taxonomy categorization, and identifying underexplored research areas, my analysis provides a new dimension that complements the body of LLM research. 3 Methodology I began the analyses with importing the Pandas li- brary, a powerful tool for data manipulation and analysis in Python. I loaded the dataset, stored as a CSV file, into a Pandas DataFrame for easier handling and manipulation of the structured data. This initial step helped verify that the data had been correctly loaded, providing an understanding of the dataset’s structure, which included columns like "Taxonomy," "Title," "Authors," "Release Date," and "Summary." This analysis is illustrated in Fig- ure 1. This preparation laid the foundation for the subsequent stages of data exploration and re- porting. Later on, the exploration phase involved examining trends in survey paper publications over time, with a focus on the "Release Date" field, and analyzing the distribution of research across vari- ous taxonomy categories. To better understand the data, descriptive statistics were computed for key fields like "Release Year" and "Taxonomy." For the data manipulation phase, textual data from the "Ti- tle" and "Summary" fields were transformed into numerical representations using Term Frequency- Inverse Document Frequency (TF-IDF) vectoriza-tion, while the categorical "Categories" field was processed using one-hot encoding. These transfor- mations were combined to build a feature matrix, which was then normalized to ensure that all fea- tures were on a consistent scale. In the final phase, a logistic regression model was implemented to predict the taxonomy category of each paper, and its performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. To ad- dress class imbalance, class-weighting techniques were applied to the model, improving its ability to classify underrepresented categories. This ap- proach provided a comprehensive understanding of the dataset and facilitated the development of a predictive model for classifying survey papers on Large Language Models (LLMs). 3.1 Data Exploration 3.1.1 Survey Paper Trends Over Time To analyze trends in survey paper publications over time, I examined the "Release Date" column in the dataset to track how the volume of published papers changed. Methodology: The "Release Date" column was first converted into a datetime format using Pan- das, making it easier to manipulate time-based data. From the converted dates, I extracted the "YearMonth" component, allowing the data to be grouped by month and the number of survey papers published each month to be counted. This group- ing method was essential for visualizing trends in publication rates. I used Matplotlib to generate a plot, where the X-axis represented the publication year and month, and the Y-axis displayed the number of survey pa- pers published in that time frame. This analysis is illustrated in Figure 2. Results: The plot revealed that very few sur- vey papers were published in 2021, with a grad- ual increase starting in early 2022. A significant spike occurred in late 2023, peaking in November 2023, where nearly 18 survey papers were pub- lished. This sharp increase reflects the growing in- terest and research activity in LLMs, likely driven by advancements in AI technologies. The trend indicates a clear rise in publication numbers, show- ing a surge in research focus on LLMs, especially in the second half of 2023. 3.1.2 Taxonomy Distribution I analyzed the distribution of survey papers across the various categories within the proposed taxon- Figure 1: Dataset in a tabular format omy to identify which areas of research were dom- inant and which were underrepresented. Methodology: To calculate the frequency of papers assigned to each category, I used the value_counts() function on the "Taxonomy" col- umn. The resulting counts gave a clear overview of how many papers were categorized under each taxonomy label. A bar chart was created using Matplotlib to vi- sualize the distribution. The X-axis represented taxonomy categories, while the Y-axis showed the number of papers in each category. This analysis is illustrated in Figure 3. Results: The bar chart revealed that the "Trust- worthy" category was the most common, with 26 papers. Other frequently represented categories included "Comprehensive" and "Prompting," both showing a relatively high number of papers. How- ever, categories such as "Law," "Finance," and "Ed- ucation" were underrepresented, suggesting that fewer survey papers focused on these areas. The results highlighted the research focus on trustwor- thiness and prompting in LLMs while identifying potential gaps in other fields like finance and edu- cation, where fewer studies were published.3.1.3 Descriptive Statistics on Release Years To better understand the temporal distribution of survey papers, I computed descriptive statistics on the "Release Year" column. This analysis provided insights into the central tendencies and spread of the data. Methodology: The "Release Date" column was first converted from string format into a datetime format using Pandas, and the "Release Year" was extracted for each paper. I computed several key statistical measures for the "Release Year" data: Mean: The average year of publication, giving insight into the central tendency. Median: The middle year of publication, which helps clarify the concentration of the data. Variance and Standard Deviation: These metrics describe the spread of publication years around the mean. Skewness: The asymmetry of the distribution, indi- cating whether papers were concentrated in earlier or more recent years. Range and Interquartile Range (IQR): These values capture the earliest and latest years of publication, as well as the spread of the middle 50% of the data. Quantiles: The 25th, 50th, and 75th percentiles Figure 2: Trends in Survey Paper Publications Over Time Figure 3: Distribution of Survey Papers by Taxonomy Category were calculated to understand how the data is dis- tributed. Results: This analysis is illustrated in Figure 4. The computed descriptive statistics showed a mean release year of 2023, confirming that the major- ity of survey papers were published recently. The skewness value suggested a slight rightward skew, indicating that most publications were concentrated in the last few years, especially in 2023 and 2024. This analysis provided a clearer picture of the pub- lication trends over time and helped interpret the temporal aspects of LLM-related research. Figure 4: Descriptive Statistics of Survey Paper Release Years 3.1.4 Visualizing Categorical and Temporal Data In the final part of data exploration, I examined the frequency distribution of categories and the temporal distribution of the release years for the survey papers. This analysis is illustrated in Figure 6, 7 and 8. Methodology: For the categorical data, I com- puted frequency counts for the "Categories" col- umn to understand which categories were most represented. This count revealed that categories like "cs.CL" and "cs.AI" were highly represented, with 28 and 27 papers respectively. To visualize the results, I created a bar chart to display the distribution of categories. The chart showed that only a few categories accounted for most of the papers, while several others were sparsely represented. To analyze the temporal data, I plotted a his- togram of release years and a cumulative distribu- tion plot. The histogram depicted the frequency of papers published in each year, while the cumulative Figure 5: Categories Frequency Distribution distribution plot provided an overall view of how the number of publications increased over time. Results: The histogram and cumulative distribu- tion plot confirmed that most survey papers were published in 2023, with few papers published in earlier years such as 2021 and 2022. The rapid in- crease in publications starting in 2023 underscored a significant rise in research efforts within the field. The combination of categorical and temporal visu- alizations gave valuable insights into both the areas of focus and the timing of research in LLM-related studies. Figure 6: Cumulative Distribution of Survey Paper Pub- lications Over Time Figure 7: Bar Chart of Categories Frequency Distribu- tion 3.2 Data Manipulation 3.2.1 Building a Feature Matrix In this stage of the analysis, I created a feature matrix for the dataset by transforming categorical and text data into numerical representations that could be used for further analysis or modeling. This process involved the use of techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and one-hot encoding. Figure 8: Cumulative Distribution of Paper Release Years Over Time Methodology: Loading the Dataset: I first loaded the dataset from the CSV file into a Pandas DataFrame for manipulation and analysis. Building the Feature Matrix: To create a robust feature matrix, I applied the following transforma- tions: TF-IDF Vectorization of Text Data: The "Title" and "Summary" columns, which contain textual data, were transformed using the TF-IDF vector- izer. This method converts the textual data into numerical vectors that capture the importance of each word relative to the rest of the dataset. The TF-IDF scores were then stored in a dense matrix form for each paper title and summary. One-Hot Encoding of Categorical Data: The "Categories" column, which contains categorical data, was processed using the one-hot encoding technique. This method transforms each unique category into a binary feature, where a value of "1" indicates the presence of a category for a particular paper and "0" indicates its absence. The resulting one-hot encoded matrix was then combined with the TF-IDF matrices. Combining Features: Once the text and categor- ical data had been vectorized, I concatenated the resulting matrices into a single feature matrix. This final matrix included the TF-IDF representations of the "Title" and "Summary" columns as well as the one-hot encoded "Categories" column, creating a comprehensive numerical representation of the dataset. Displaying the Feature Matrix: Finally, I dis- played the combined feature matrix (figure 9), which had 3,748 columns representing all the fea- tures extracted from the textual and categorical data. The large number of columns reflects the complexity of the dataset, with each column cor- responding to either a unique word in the text or a binary category label. This data manipulation step was crucial in preparing the dataset for further analysis or machine learning tasks by converting all text and categorical data into a format that can be processed by algorithms. The feature matrix served as the foundation for modeling and deeper exploration of the dataset’s properties. 3.2.2 Normalization of Feature Matrix In the subsequent step of data manipulation, I nor- malized the feature matrix to ensure all features were on the same scale and prepared for further machine learning algorithms. Normalization is es- sential because many machine learning models are sensitive to the magnitude of different features, and this process ensures that no feature dominates the others due to its scale. Methodology: Converting Boolean Columns: Initially, I con- verted the boolean columns within the feature ma- trix into numeric values (1s and 0s). This was achieved by applying the pd.to_numeric() func- tion to ensure that all columns in the matrix were numeric and ready for scaling. Any errors during conversion were handled by filling in missing val- ues with zeros, ensuring a clean dataset. Normalization with MinMaxScaler: To normal- ize the numeric data, I applied the MinMaxScaler from Scikit-learn, which scales all features to a range between 0 and 1. The MinMaxScaler was ap- plied only to the numeric values of the DataFrame to preserve the feature matrix’s structure. This scal- ing ensures that features are comparable, regardless of their original scale. Reconstructing the DataFrame: After normal- ization, the scaled values were converted back into a Pandas DataFrame, preserving the column names and structure of the original feature matrix. This made it easier to interpret the scaled values and ensured compatibility with future operations. Displaying the Normalized Matrix: The result- ing normalized matrix was displayed (figure 10), showing that all values across the 3,748 columns (features) were now scaled between 0 and 1. This step confirmed that the normalization process had successfully standardized the dataset, ensuring that each feature contributed equally to any further anal- ysis or modeling. This normalization step was crucial for ensur- ing that no individual feature would disproportion- ately affect the results of machine learning models.By scaling all features to the same range, the data was made ready for a variety of machine learning techniques, which often assume normalized input features for optimal performance. 3.2.3 Encoding Labels Using LabelEncoder In the next stage of data manipulation, I used the LabelEncoder from Scikit-learn to transform the categorical "Taxonomy" labels into numerical val- ues. This transformation is essential for converting non-numeric data into a format that can be fed into machine learning algorithms, which typically re- quire numeric input. This analysis is illustrated in Figure 11. Methodology: Initializing the LabelEncoder: I first initialized the LabelEncoder to convert the categorical labels in the "Taxonomy" column. The "Taxonomy" col- umn contained the different categories to which each paper was assigned, and I assigned this col- umn to the variable y. Fitting and Transforming Labels: The LabelEn- coder was then used to fit and transform the tax- onomy labels. This process encoded each unique taxonomy category as an integer. For example, the label "Comprehensive" was encoded as "1," and "Trustworthy" was encoded as "7." This transfor- mation allowed me to represent the categorical la- bels numerically, making the dataset more suitable for machine learning models. Checking Encoded Labels: After the transfor- mation, I printed both the encoded labels and the original labels to verify the correctness of the en- coding process. This step ensured that the mapping between the original taxonomy categories and the corresponding integers was accurate. Inverse Transforming Labels: To validate the encoding, I also used the inverse_transform method of the LabelEncoder to map the encoded integers back to their original categorical labels. This allowed me to confirm that the encoded la- bels could be correctly reverted to their original taxonomy categories if necessary. This encoding step was crucial for transforming the categorical taxonomy data into a numerical for- mat, which is required for many machine learning algorithms. By using LabelEncoder, I was able to retain the essential categorical information while making the data more computationally manageable for future analysis or modeling tasks. Figure 9: Featured Matrix Figure 10: Normalized matrix Figure 11: Visualization of Encoded and Original Taxonomy Labels for Survey Papers Figure 12: Dataset Overview: Training and Testing Data Shapes 3.2.4 Splitting the Data into Training and Testing Sets In this step, I prepared the dataset for model train- ing by splitting it into training and testing sets. This process is crucial for evaluating how well a ma- chine learning model generalizes to unseen data by using one portion of the data for training and another for testing. I used the train_test_split function from Scikit-learn to carry out this opera- tion.This analysis is illustrated in Figure 12. Methodology: Defining the Test Ratio: I set the test ratio to 0.4, meaning that 40% of the data would be reserved for testing, and the remaining 60% would be used for training. This split ratio ensures that a signifi- cant portion of the data is set aside to evaluate the model’s performance on unseen data. Splitting the Data: The train_test_split function was used to split the normalized feature matrix ( X) and the encoded labels ( y_encoded ). The function randomly split the data according to the specified ratio while maintaining the struc- ture and shape of the data. A random seed (random_state=42 ) was set to ensure reproducibil- ity of the split. -X_train andy_train : These represent the training set features and labels. - X_test and y_test : These represent the testing set features and labels. Verifying the Split: After splitting the data, I checked the shapes of both the training and testing sets to ensure that the split was done correctly. The training data had 86 samples, while the testing data had 58 samples, consistent with the 60/40 split. -Training Data Shape : (86, 3748), meaning 86 samples with 3,748 features each. - Testing Data Shape : (58, 3748), meaning 58 samples with 3,748 features each. This step was essential for preparing the data for machine learning, ensuring that the model would be trained on one portion of the data and validated on another to avoid overfitting and assess its gener- alization performance. 3.3 Data Evaluation 3.3.1 Logistic Regression Model To evaluate the model’s performance, a logistic re- gression algorithm was implemented to predict the taxonomy category for each paper in the dataset. Logistic regression is suitable for multi-class clas- sification problems and provides insight into therelationships between features and labels. This analysis is illustrated in Figure 13. Methodology: •Converting Column Names: The column names in the feature matrix were converted to strings to ensure compatibility with Scikit- learn. •Model Initialization: The logistic regression model was initialized using Scikit-learn’s LogisticRegression function. The max pa- rameter was set to 1000 to allow sufficient iterations for model convergence. •Training the Model: The model was trained on the training dataset using the fit() method to establish patterns between features and tax- onomy labels. •Making Predictions: After training, predic- tions were made on the test dataset using the predict() method, aiming to classify each paper into its respective taxonomy category. •Evaluating Performance: Model performance was evaluated using the accuracy_score function, with the overall accuracy measured at 43%. A classification report was generated, providing precision, recall, and F1-score for each category. •Addressing Class Imbalance: The label distri- bution in both the training and testing datasets was reviewed to assess class imbalance, which can negatively impact model performance, es- pecially for underrepresented categories. The model’s performance highlighted areas for improvement, particularly regarding class imbal- ance and the need for more complex models to enhance predictive accuracy. 3.3.2 Confusion Matrix and Model Visualization To further assess the logistic regression model, a confusion matrix was generated. This matrix pro- vided detailed insights into the number of correct and incorrect classifications for each taxonomy cat- egory. This is illustrated in Figure 14 and Figure 15. Methodology: •Classification Report: A classification report was created to summarize precision, recall, Figure 13: Classification Report and Label Distributionand F1-scores for each class, offering a more comprehensive view of the model’s perfor- mance. •Confusion Matrix: A confusion matrix was computed using the confusion matrix function to compare true labels with predicted labels across all classes. •Confusion Matrix Visualization: The confu- sion matrix was visualized as a heatmap us- ing the Seaborn library. The darker colors along the diagonal of the heatmap indicated correctly predicted classes, while off-diagonal elements reflected misclassifications. •Model Accuracy: The overall model accuracy remained consistent at 43 The confusion matrix and visual representa- tion helped identify which classes were mis- classified more frequently, highlighting poten- tial areas where the model could be improved. Figure 14: Classification Report with Confusion Matrix 3.3.3 Bonus Question: Logistic Regression with Class Weighting Given the class imbalance in the dataset, class weighting was applied to the logistic regres- Figure 15: Confusion Matrix Heatmap sion model to improve performance, particu- larly for underrepresented categories. This is illustrated in Figure 16. Methodology: –Installing Imbalanced-Learn: The imbalanced-learn library was in- stalled to manage class imbalances during training. –Initializing Logistic Regression with Class Weighting: The logistic regres- sion model was re-initialized with the class_weight=’balanced’ parameter, ensuring that classes were weighted in- versely proportional to their frequency in the dataset. –Training the Model: The model was re- trained on the dataset with the adjusted class weights to address the imbalance and improve classification of underrepre- sented categories. –Making Predictions and Evaluating Per- formance: Predictions were made on the test dataset, and the accuracy improved to 47%. Another classification report was generated to evaluate improvements in the precision, recall, and F1-score for underrepresented classes. Results: The adjusted logistic regression model showed a modest improvement in over- all accuracy, from 43% to 47%, and improved performance on underrepresented classes, al- though certain categories still exhibited lower performance. Figure 16: Class weighing 4 Conclusion In this report, I analyzed survey papers on Large Language Models (LLMs) by explor- ing metadata, categorizing research areas, and using machine learning to predict taxonomy categories. The analysis highlighted publica- tion trends, with significant research growth in 2023, particularly in "Trustworthy" and "Prompting" areas. I implemented a logistic regression model, initially achieving 43% accuracy, which im- proved to 47% after applying class-weighting techniques to address class imbalance. Al- though this improved classification for under- represented categories, further refinement is needed to enhance performance in some areas. This study provides a foundation for under- standing LLM survey papers and offers a machine learning-based approach to help re- searchers identify key trends and gaps in the field. Future work could involve more ad- vanced models or additional metadata to im- prove classification and insight into LLM re- search. A APPENDIX