Recent advancements in large language models have led to the publication of numerous survey papers, offering insights into their development and applications. However, for beginners, the vast amount of literature can be overwhelming, posing challenges in identifying key trends and understanding foundational concepts efficiently. This report explores metadata from recent LLM survey papers, aiming to provide an in-depth analysis of publication trends, popular topics, and accessibility for newcomers. Our findings offer guidance on how to navigate the large body of work on LLMs, enhancing the abil- ity of new researchers to engage with the field effectively. 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 Mod- els (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 beginners are interested in the research top- ics about LLMs. To learn the recent progress in this field, new beginners commonly will read sur- vey papers about LLMs. Therefore, to facilitate their learning, numerous survey papers on LLMs have been published in the last two years. How- ever, a large amount of these survey papers can be overwhelming, 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 understanding (Zhuang and Kennington, 2024). 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). In particular, the explosion of LLM survey pa- pers in recent years has created a wealth of informa- tion for newcomers to the field. However, the sheer volume of surveys poses a challenge for beginners who want to quickly grasp the essential trends and topics. To address this challenge, this project ex- plores and analyzes metadata from LLM survey papers, providing insights to enhance their accessi- bility and understanding (Zhuang and Kennington, 2024). Specifically, we aim to investigate the metadata from a dataset of LLM survey papers, which in- cludes attributes such as publication dates, topics, and categories. By analyzing these metadata at- tributes, we will identify patterns in the frequency of publications, the popularity of topics, and trends over time. We will also assess the accessibility of the papers in terms of readability and relevance to beginner audiences. Through this exploration, we provide recommendations for selecting survey papers based on individual needs and learning ob- jectives. Overall, our contributions can be summarized as follows: •We provide a detailed analysis of the metadata from LLM survey papers to uncover trends in publication frequency, popular topics, and accessibility. •We identify key patterns in the metadata that can help beginners navigate the growing body of LLM surveys. •We offer recommendations on improving sur- vey paper accessibility for newcomers to the LLM research field. 2 Related Work The growing interest in LLMs has led to a signifi- cant number of survey papers published in the last few years. Studies like (Zhuang and Al Hasan, 2022) focus on defending LLMs from adversarial attacks, while others like (Bai et al., 2022) explore model alignment with human values. While these surveys provide deep insights into LLMs, few stud- ies have focused on exploring the metadata of these papers for trends and accessibility for newcomers. Our work fills this gap by analyzing the metadata associated with LLM survey papers and offering a more accessible entry point for new researchers. 3 Methodology 3.1 Data Exploration The dataset we use for this study consists of meta- data from survey papers related to LLMs, includ- ing attributes such as Taxonomy ,Title,Authors , Release Date ,Links ,Paper ID ,Categories , and Summary . Our first step is to explore this metadata to understand the distribution of survey papers over time and by topic. Figure 1: Trends in LLM Survey Papers over time. In Figure 1, we show the distribution of survey papers published from 2021 to 2024, revealing a significant increase in publications, particularly in 2023. This reflects the growing interest in LLMs and the need for comprehensive reviews of the field. 3.2 Data Manipulation For our data manipulation phase, we categorize the survey papers based on topics and their relevance to beginners. This involves extracting keywords from the paper titles and summaries, categorizing them into major themes such as model alignment ,inter- pretability ,applications , and training techniques .This classification helps beginners identify which papers might be most helpful based on their spe- cific interests. 3.3 Data Evaluation To evaluate the accessibility of survey papers, we assess each paper based on several factors, includ- ing readability (measured through an automated Flesch-Kincaid score), length, and the number of citations. We find that papers focusing on practi- cal applications tend to have a lower readability score, making them more approachable for begin- ners, while papers focusing on model internals and training techniques are more advanced. Category Avg. Readability Score Avg. Length (pages) Applications 45 12 Training Techniques 30 18 Model Alignment 35 15 Table 1: Readability and Length of Survey Papers by Category. As shown in Table 1, papers focused on appli- cations are generally more accessible, with higher readability scores and shorter lengths compared to papers focused on training techniques andmodel alignment . 4 Research and Analysis In this section, we present the research findings and analysis based on the metadata of LLM sur- vey papers. The figures included below illustrate key trends, patterns, and insights derived from the dataset. 4.1 Distribution of Proposed Taxonomy The following bar chart illustrates the distribution of the proposed taxonomy for the LLM survey pa- pers. Figure 2 shows the frequency of various cate- gories within the proposed taxonomy, highlighting the relative prominence of each category in the surveyed papers. 4.2 Top 10 Most Frequent Categories The following figure presents the top 10 most fre- quent categories identified in the LLM survey pa- pers. Figure 2: Distribution of the proposed taxonomy for LLM survey papers. Figure 3: Top 10 most frequent categories in LLM sur- vey papers. As shown in Figure 3, the most prevalent cate- gories reflect current research trends and interests within the field of large language models. 4.3 Cosine Similarity Heatmap The cosine similarity heatmap for the first 10 doc- uments provides insights into the relationships be- tween them based on their content. Figure 4: Cosine Similarity Heatmap for the first 10 documents. Figure 4 illustrates the cosine similarity between documents, revealing how closely related the con- tents of these documents are. Darker colors indi- cate higher similarity, while lighter colors representlower similarity. 4.4 Machine Learning Models To further analyze the dataset, we employed ma- chine learning models, specifically Support Vector Machines (SVM) and Random Forest. 4.4.1 Support Vector Machine We began our analysis with the Support Vector Ma- chine (SVM) model, which is a supervised learning algorithm commonly used for classification tasks. The SVM model was trained on the training dataset, using a linear kernel and balanced class weights to address any class imbalance. The model was then evaluated on the test dataset, and the accuracy score was calculated. The SVM achieved an accuracy of 0.29. 4.4.2 Random Forest Classifier Next, we implemented the Random Forest classi- fier, another supervised learning algorithm that op- erates by constructing multiple decision trees dur- ing training and outputting the mode of the classes for classification tasks. The Random Forest model was also trained on the training dataset and subse- quently evaluated on the test dataset. The accuracy score for the Random Forest model was found to be 0.26. 4.4.3 Tuning Random Forest To improve the performance of the Random Forest model, we performed hyperparameter tuning using Grid Search. This involved testing various combi- nations of hyperparameters, such as the number of estimators, maximum depth of the trees, and other parameters. After tuning, the accuracy of the Ran- dom Forest model improved significantly, reaching 0.48. 5 Results The results of the model evaluations are summa- rized as follows: • SVM Accuracy: 0.29 • Random Forest Accuracy: 0.26 • Tuned Random Forest Accuracy: 0.48 6 Conclusion In this report, we explored the metadata from a dataset of LLM survey papers to uncover trends and insights that can assist beginners in navigating the large volume of LLM literature. Our analy- sis identified key patterns in publication frequency, topic distribution, and paper accessibility, provid- ing useful guidance for selecting appropriate survey papers based on individual research interests. Our findings highlight the importance of curating acces- sible surveys for newcomers, while also showing the need for clear categorization of papers based on their target audience and technical depth. A APPENDIX In this section, we provide details about the config- urations and parameters used during our data ex- ploration, manipulation, and evaluation processes. A.0.1 Dataset Information The dataset used for this project consists of meta- data from survey papers on large language models (LLMs). The columns included in the dataset are as follows: •Taxonomy : Classification of the paper (e.g., applications, training techniques, model align- ment). •Title : Title of the paper. •Authors : List of authors. •Release Date : Date when the paper was pub- lished. •Links : URL to access the paper. •Paper ID : A unique identifier for each paper. •Categories : Keywords or topics related to the paper. •Summary : Abstract or summary of the paper. A.0.2 Hyperparameters for Readability Evaluation For readability evaluation, we employed the Flesch- Kincaid readability tests. The following settings were used: Flesch Reading Ease Formula: Score = 206.835 - 1.015 ×totalwords totalsentences − 84.6× totalsyllables totalwords A higher score indicates easier readability. Papers with scores between 50–60 are considered fairly easy to read, while scores below 30 indicate advanced difficulty.Flesch-Kincaid Grade Level Formula: Grade level = 0.39 ×totalwords totalsentences + 11 .8× totalsyllables totalwords −15.59 This formula estimates the U.S. grade level required to understand the text. A.0.3 Data Manipulation Process For data manipulation, we used Python’s pandas library to filter, group, and analyze the dataset. The following key operations were performed: •Topic Extraction : Keywords in the paper’s titles and summaries were extracted to classify papers into the main categories (e.g., applica- tions, training techniques, etc.). •Grouping by Release Date : We grouped the papers by their release dates to analyze the trends in LLM surveys over time. •Readability and Length Calculation : For each paper, the number of words, sentences, and syllables was calculated using Python’s ‘textstat‘ library to determine readability. A.0.4 Evaluation Metrics To evaluate the accessibility of survey papers, we considered the following metrics: •Readability Score : Based on the Flesch Read- ing Ease formula. •Average Paper Length : The average num- ber of pages for each category of survey pa- pers (applications, training techniques, model alignment, etc.). •Number of Citations : The number of cita- tions each paper received, providing a proxy for relevance and influence in the field. A.1 Further Analysis Tools We used additional libraries and software tools for specific tasks: •matplotlib : Used to generate graphs showing trends in publication over time. •seaborn : A Python library for creating more visually appealing statistical graphics. •Natural Language Toolkit (NLTK) : Used for tokenization and extraction of features such as sentence count, word count, and sylla- ble count for readability analysis. A.2 Limitations and Future Work The analysis performed in this project is primarily focused on metadata, which limits the depth of insights into the actual content of the survey papers. Future work could include: •Analyzing the full text of survey papers to understand specific contributions and research directions. •Expanding the dataset to include more re- cent papers or papers from a broader range of sources. •Implementing advanced natural language pro- cessing techniques such as topic modeling or sentiment analysis to enhance understanding of trends in LLM research.