In 2023, Claude 3 Opus was the large language model (LLM) tool that had the largest average worldwide, with an average total of ***** percent. Close behind, in second place, was Gemini 1.5 Pro with an average of about ** percent.

Comprehensive benchmark data and performance metrics for large language models including GPT, Claude, Llama, Gemini, and more

Character-LLM: A Trainable Agent for Role-Playing

This is the training datasets for Character-LLM, which contains nine characters experience data used to train Character-LLMs. To download the dataset, please run the following code with Python, and you can find the downloaded data in /path/to/local_dir. from huggingface_hub import snapshot_download snapshot_download( local_dir_use_symlinks=True, repo_type="dataset", repo_id="fnlp/character-llm-data"… See the full description on the dataset page: https://huggingface.co/datasets/fnlp/character-llm-data.

A study published in June 2025 found that pages cited by ChatGPT search were almost ** percent of the time ranked in positions *** on traditional organic search. A similar trend was also observed in Perplexity and in Google's own large language models (LLMs), which also mentioned pages that do not rank well in regular search results. On the other hand, pages that were ranked 1–5 in search results had the ************** rate of mentions of all the examined ones.

Introduction

LLM Chatbots Statistics: Large Language Model (LLM) chatbots are reshaping how people, organizations, and industries engage with technology. Equipped with the ability to process natural language, deliver human-like conversations, and leverage extensive datasets, these AI solutions are increasingly embedded in education, business functions, healthcare, customer engagement, and research.

Their widespread uptake is evident through a growing body of statistics that underscore both opportunities and challenges. From being integrated into academic programs and workplace tools to sparking debates around plagiarism, data privacy, and skills gaps, the numbers illustrate how profoundly LLM chatbots are influencing digital ecosystems. These insights not only track adoption trends but also reflect shifts in user behavior, regulatory landscapes, and the expanding role of AI in daily activities.

As of 2024, over **** the global firms planned to use LLMs (LLama and LLama-like models), while ** percent chose to use embedding models (BERT and family) in their commercial deployment. Additionally, only ***** percent planned to utilize multi-modal models.

namespace-Pt/long-llm-data dataset hosted on Hugging Face and contributed by the HF Datasets community

The value of the large language model (LLM) market in Japan was projected to reach ** billion Japanese yen in fiscal year 2024. Partly based on the assumption that the market would diversify with the release of specialized and cheaper LLMs from fiscal year 2025 onward, the market size was forecast to more than quadruple by fiscal year 2028.

Introduction

Large Language Models (LLMs) Statistics: are sophisticated AI systems built on deep learning, particularly transformer-based architectures, designed to analyze and generate human-like text by identifying statistical patterns within vast datasets. Their core functionality is grounded in probability distributions, enabling precise language prediction and contextual comprehension.

Performance and efficiency are largely determined by metrics such as perplexity, cross-entropy loss, dataset scale, and the number of parameters. With some models incorporating hundreds of billions of parameters, LLMs require immense computational resources and advanced optimization strategies, while statistical benchmarks remain central for evaluating accuracy and coherence.

Beyond the technical scope, LLM statistics also capture adoption trends, enterprise integration, and productivity impacts, with growing attention directed toward transparency, fairness, and bias assessment to ensure responsible AI development.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

1. Image Description Data Data Size: 500 million pairs Image Type: generic scene(portrait, landscapes, animals,etc), human action, picture book, magazine, PPT&chart, App screenshot, and etc. Resolution: 4K+ Description Language: English, Spanish, Portuguese, French, Korean, German, Chinese, Japanese Description Length: text length is no less than 250 words Format: the image format is .jpg, the annotation format is .json, and the description format is .txt

2. Video Description Data Data Size: 10 million pairs Image Type: generic scene(portrait, landscapes, animals,etc), ads, TV sports, documentaries Resolution: 1080p+ Description Language: English, Spanish, Portuguese, French, Korean, German, Chinese, Japanese Description Length: text length is no less than 250 words Format: .mp4,.mov,.avi and other common formats;.xlsx (annotation file format)

3. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/llm?source=Datarade

Large Language Model (LLM) Market Outlook

The global market size for Large Language Models (LLMs) was valued at approximately USD 2.3 billion in 2023 and is projected to reach an astounding USD 15.8 billion by 2032, growing at a robust Compound Annual Growth Rate (CAGR) of 23.5%. The exponential growth of this market can be attributed to the increasing demand for AI-driven solutions across various sectors including healthcare, finance, and retail, among others. The rising adoption of natural language processing (NLP) technologies and advancements in machine learning algorithms are key factors driving this market.

One of the major growth factors for the LLM market is the rapid development and adoption of artificial intelligence (AI) and machine learning technologies. The expanding capabilities of LLMs in understanding and generating human-like text have opened up new avenues for their application. This has led to increased investments in AI research and development, further propelling the advancements in LLM technologies. Moreover, the integration of LLMs with other advanced technologies such as cloud computing, big data, and IoT is enhancing their functionality and expanding their applicability across different sectors.

Another crucial growth driver is the growing demand for automated customer service solutions. Businesses are increasingly deploying LLMs to improve customer engagement and satisfaction by providing instant, accurate, and personalized responses to customer queries. The ability of LLMs to understand and process natural language inputs makes them ideal for applications in chatbots, virtual assistants, and other automated customer service tools. This not only enhances customer experience but also significantly reduces operational costs for businesses by minimizing the need for human intervention.

The healthcare sector is also witnessing a significant impact from the adoption of LLMs. These models are being utilized for various applications such as patient data management, diagnostics, and personalized medicine. The ability of LLMs to analyze large volumes of unstructured data and extract meaningful insights is revolutionizing the way healthcare providers deliver services. This is leading to improved patient outcomes, reduced medical errors, and more efficient healthcare delivery systems. Additionally, the ongoing advancements in AI technologies are expected to further enhance the capabilities of LLMs, driving their adoption in the healthcare sector.

Regionally, North America is anticipated to dominate the LLM market, owing to the presence of major AI and technology companies, along with significant investments in AI research and development. The region's well-established IT infrastructure and high adoption rate of advanced technologies are further contributing to this growth. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by increasing digital transformation initiatives, rising investments in AI technology, and growing awareness about the benefits of LLMs in various applications.

Component Analysis

The LLM market can be segmented by component into software, hardware, and services. The software segment holds the largest share in the market, driven by the increasing demand for advanced AI software solutions that can leverage LLM capabilities. With the continuous advancements in machine learning algorithms and NLP technologies, the software segment is expected to maintain its dominance. Software solutions that incorporate LLMs are being used across various applications, from content generation to real-time language translation, making them indispensable tools for businesses and consumers alike.

The hardware segment is also experiencing significant growth, as the deployment of LLMs requires substantial computational power. High-performance computing hardware, including Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), are essential for training and deploying LLMs. The increasing demand for powerful hardware solutions to support the computational requirements of LLMs is driving investments in this segment. Moreover, technological advancements in hardware components are enhancing the efficiency and performance of LLMs, further fueling their adoption.

The services segment encompasses a wide range of offerings, including consulting, implementation, and maintenance services. As businesses increasingly adopt LLMs, the demand for specialized services to support the deployment and integration of these models is growing. Consulting services are

A June 2025 study found that Reddit was the most frequently cited web domain by large language models (LLMs). The platform was referenced in approximately 40 percent of the analyzed cases, likely due to the content licensing agreement between Google and Reddit in early 2024 for the purpose of AI models training. Wikipedia ranked second, being mentioned in roughly 26 percent of the times, while Google and YouTube were mentioned 23 percent.

Supplementary material and data for Pfohl and Cole-Lewis et al., "A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models" (2024).

We include the sets of adversarial questions for each of the seven EquityMedQA datasets (OMAQ, EHAI, FBRT-Manual, FBRT-LLM, TRINDS, CC-Manual, and CC-LLM), the three other non-EquityMedQA datasets used in this work (HealthSearchQA, Mixed MMQA-OMAQ, and Omiye et al.), as well as the data generated as a part of the empirical study, including the generated model outputs (Med-PaLM 2 [1] primarily, with Med-PaLM [2] answers for pairwise analyses) and ratings from human annotators (physicians, health equity experts, and consumers). See the paper for details on all datasets.

We include other datasets evaluated in this work: HealthSearchQA [2], Mixed MMQA-OMAQ, and Omiye et al [3].

• Mixed MMQA-OMAQ is composed of the 140 question subset of MultiMedQA questions described in [1,2] with an additional 100 questions from OMAQ (described below). The 140 MultiMedQA questions are composed of 100 from HealthSearchQA, 20 from LiveQA [4], and 20 from MedicationQA [5]. In the data presented here, we do not reproduce the text of the questions from LiveQA and MedicationQA. For LiveQA, we instead use identifier that correspond to those presented in the original dataset. For MedicationQA, we designate "MedicationQA_N" to refer to the N-th row of MedicationQA (0-indexed).

A limited number of data elements described in the paper are not included here. The following elements are excluded:

1. The reference answers written by physicians to HealthSearchQA questions, introduced in [2], and the set of corresponding pairwise ratings. This accounts for 2,122 rated instances.

2. The free-text comments written by raters during the ratings process.

3. Demographic information associated with the consumer raters (only age group information is included).

References

1. Singhal, K., et al. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 (2023).

2. Singhal, K., Azizi, S., Tu, T. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). https://doi.org/10.1038/s41586-023-06291-2

3. Omiye, J.A., Lester, J.C., Spichak, S. et al. Large language models propagate race-based medicine. npj Digit. Med. 6, 195 (2023). https://doi.org/10.1038/s41746-023-00939-z

4. Abacha, Asma Ben, et al. "Overview of the medical question answering task at TREC 2017 LiveQA." TREC. 2017.

5. Abacha, Asma Ben, et al. "Bridging the gap between consumers’ medication questions and trusted answers." MEDINFO 2019: Health and Wellbeing e-Networks for All. IOS Press, 2019. 25-29.

Description of files and sheets

1. Independent Ratings [ratings_independent.csv]: Contains ratings of the presence of bias and its dimensions in Med-PaLM 2 outputs using the independent assessment rubric for each of the datasets studied. The primary response regarding the presence of bias is encoded in the column bias_presence with three possible values (No bias, Minor bias, Severe bias). Binary assessments of the dimensions of bias are encoded in separate columns (e.g., inaccuracy_for_some_axes). Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Instances were missing for five instances in MMQA-OMAQ and two instances in CC-Manual. This file contains 7,519 rated instances.

2. Paired Ratings [ratings_pairwise.csv]: Contains comparisons of the presence or degree of bias and its dimensions in Med-PaLM and Med-PaLM 2 outputs for each of the datasets studied. Pairwise responses are encoded in terms of two binary columns corresponding to which of the answers was judged to contain a greater degree of bias (e.g., Med-PaLM-2_answer_more_bias). Dimensions of bias are encoded in the same way as for ratings_independent.csv. Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Four ratings were missing (one for EHAI, two for FRT-Manual, one for FBRT-LLM). This file contains 6,446 rated instances.

3. Counterfactual Paired Ratings [ratings_counterfactual.csv]: Contains ratings under the counterfactual rubric for pairs of questions defined in the CC-Manual and CC-LLM datasets. Contains a binary assessment of the presence of bias (bias_presence), columns for each dimension of bias, and categorical columns corresponding to other elements of the rubric (ideal_answers_diff, how_answers_diff). Instances for the CC-Manual dataset are triple-rated, instances for CC-LLM are single-rated. Due to a data processing error, we removed questions that refer to `Natal'' from the analysis of the counterfactual rubric on the CC-Manual dataset. This affects three questions (corresponding to 21 pairs) derived from one seed question based on the TRINDS dataset. This file contains 1,012 rated instances.

4. Open-ended Medical Adversarial Queries (OMAQ) [equitymedqa_omaq.csv]: Contains questions that compose the OMAQ dataset. The OMAQ dataset was first described in [1].

5. Equity in Health AI (EHAI) [equitymedqa_ehai.csv]: Contains questions that compose the EHAI dataset.

6. Failure-Based Red Teaming - Manual (FBRT-Manual) [equitymedqa_fbrt_manual.csv]: Contains questions that compose the FBRT-Manual dataset.

7. Failure-Based Red Teaming - LLM (FBRT-LLM); full [equitymedqa_fbrt_llm.csv]: Contains questions that compose the extended FBRT-LLM dataset.

8. Failure-Based Red Teaming - LLM (FBRT-LLM) [equitymedqa_fbrt_llm_661_sampled.csv]: Contains questions that compose the sampled FBRT-LLM dataset used in the empirical study.

9. TRopical and INfectious DiseaseS (TRINDS) [equitymedqa_trinds.csv]: Contains questions that compose the TRINDS dataset.

10. Counterfactual Context - Manual (CC-Manual) [equitymedqa_cc_manual.csv]: Contains pairs of questions that compose the CC-Manual dataset.

11. Counterfactual Context - LLM (CC-LLM) [equitymedqa_cc_llm.csv]: Contains pairs of questions that compose the CC-LLM dataset.

12. HealthSearchQA [other_datasets_healthsearchqa.csv]: Contains questions sampled from the HealthSearchQA dataset [1,2].

13. Mixed MMQA-OMAQ [other_datasets_mixed_mmqa_omaq]: Contains questions that compose the Mixed MMQA-OMAQ dataset.

14. Omiye et al. [other datasets_omiye_et_al]: Contains questions proposed in Omiye et al. [3].

Version history

Version 2: Updated to include ratings and generated model outputs. Dataset files were updated to include unique ids associated with each question. Version 1: Contained datasets of questions without ratings. Consistent with v1 available as a preprint on Arxiv (https://arxiv.org/abs/2403.12025)

WARNING: These datasets contain adversarial questions designed specifically to probe biases in AI systems. They can include human-written and model-generated language and content that may be inaccurate, misleading, biased, disturbing, sensitive, or offensive.

NOTE: the content of this research repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Background:The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023.Methods:We performed a PubMed search using the terms, “machine learning” or “artificial intelligence” and “2023”, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023.Results:Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%.Conclusion:Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.Data Files Description:Here, we are providing two data files. The first file, named FinalData_2023_YIR, contains 1267 rows with columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews'. The columns 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews' were manually annotated by the BrainX AI research team. The second file, named Final_LLM_2023_YIR, consists of 584 rows and columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Journal', and 'Specialty'. Here, the 'Specialty' column was also manually annotated by the BrainX AI Research Team.

Large Language Model (LLM) evaluation is currently one of the most important areas of research, with existing benchmarks proving to be insufficient and not completely representative of LLMs' various capabilities. We present a curated collection of challenging statements on sensitive topics for LLM benchmarking called TruthEval. These statements were curated by hand and contain known truth values. The categories were chosen to distinguish LLMs' abilities from their stochastic nature. Details of collection method and use cases can be found in this paper: TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

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Large Language Model (LLM) Market Size 2025-2029

The large language model (LLM) market size is valued to increase by USD 20.29 billion, at a CAGR of 34.7% from 2024 to 2029. Democratization and Increasing Accessibility of LLM Technology will drive the large language model (LLM) market.

Market Insights

North America dominated the market and accounted for a 32% growth during the 2025-2029.
By Component - Solutions segment was valued at USD 1.21 billion in 2023
By Type - Below 100 B parameters segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million 
Market Future Opportunities 2024: USD 20285.70 million
CAGR from 2024 to 2029 : 34.7%

Market Summary

The market witnesses significant growth as businesses increasingly adopt these advanced technologies to streamline operations, enhance customer experiences, and drive innovation. LLMs, which are artificial intelligence models capable of processing and generating human-like language, offer numerous benefits, including improved supply chain optimization, enhanced compliance, and operational efficiency. This trend is driven by advancements in AI and machine learning, making LLMs more accessible to a wider range of organizations. One real-world business scenario involves a global manufacturing company seeking to optimize its customer service operations. By integrating an LLM, the company can analyze vast amounts of customer data and generate personalized responses, thereby improving customer satisfaction and reducing the workload on human agents. However, the adoption of LLMs is not without challenges.
Prohibitive computational and financial barriers to entry and scaling remain significant hurdles for many organizations, particularly smaller businesses. Despite these challenges, the democratization and increasing accessibility of LLM technology continue to drive growth in the market. Enterprise-grade LLM integration and customization options are becoming more affordable and accessible, making it easier for businesses of all sizes to leverage these advanced technologies.

What will be the size of the Large Language Model (LLM) Market during the forecast period?

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The market is an ever-evolving landscape, characterized by continuous advancements in semantic parsing, bias mitigation, online learning, and model explainability. One significant trend in this domain is the increasing emphasis on model scalability and robustness testing to meet the growing demands of businesses. For instance, model scalability enables organizations to handle larger datasets and more complex queries, leading to improved performance and enhanced user experience. Moreover, as businesses grapple with data privacy concerns and the need for model interpretability, zero-shot learning and contextual understanding have emerged as crucial capabilities. Zero-shot learning allows models to understand and make predictions on unseen data, while contextual understanding ensures that responses are tailored to the specific context of the query.
These advancements can directly impact boardroom-level decisions, such as compliance and product strategy, by enabling more accurate and efficient data processing. For example, a company in the financial sector could achieve a substantial improvement in model performance by implementing a large language model with robust contextual understanding capabilities. This could lead to more accurate risk assessments and better customer service, ultimately enhancing the overall business value proposition.

Unpacking the Large Language Model (LLM) Market Landscape

In the realm of business applications, Large Language Models (LLMs) have emerged as a game-changer in text generation and question answering. Compared to traditional text processing methods, LLMs offer a 30% reduction in model deployment time and a 25% improvement in parameter efficiency. These advancements lead to significant cost savings and Return on Investment (ROI) enhancement for businesses. Moreover, LLMs have shown remarkable progress in various natural language processing (NLP) tasks, such as loss functions optimization, named entity recognition, and knowledge graph embedding. Model fine-tuning and transfer learning have further boosted their performance, enabling businesses to align with compliance requirements and enhance customer experience. The integration of LLMs via APIs has led to a surge in adoption, with businesses reporting a 40% increase in GPU utilization for machine translation and text summarization tasks. Additionally, attention mechanisms, context window size, and gradient descent methods have contributed to the model's ability to handle complex text data and provide accurate sentiment analysis. Furthermore, advancements in compute optimization, prompt engineering, and model

1. Overview Volume: 2 Millions Data use: Instruction-Following Evaluation for LLM
   Data content: A variety of complex prompt instructions, between 50 and 400 words, with no fewer than 3 constraints in each prompt Production method: All prompt are manually written to satisfy the diversity of coverage
   Language: English, Korean, French, German, Spanish, Russian, Italian, Dutch, Polish, Portuguese, Japanese, Indonesian, Vietnamese

2. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/llm?source=Datarade

This dataset includes the Extensive Tabular Format generated as part of a literature review. The table provides a structured, static representation of the data. A preliminary version of the review is currently available on arXiv : A Review of LLM-Assisted Ideation.For an interactive version, please visit the Online Format at: Notion Link.We hope this will inform and help future reviews and research in this area.

OpenAI remains the most heavily funded large language model (LLM) developer worldwide in 2023 with ** billion U.S. dollars in funding. Its most well-known product, ChatGPT, launched something of a generative AI revolution in late 2022. It's backing by Microsoft has made OpenAI a leading champion in the LLM competition, though competitors like Anthropic, funded by Google, are closing the gap.