Are you fascinated by how artificial intelligence can write natural language text? This article compares two of the most impressive language models that can produce text from user input: ChatGPT and Llama2. These models differ in their features, performance, and applications. Read the entire article to understand how they work, what they can do, and which one is right for you.
Overview and Background
|Release Date||November 2022||July 2023|
|Model Architecture||Transformer (encoder-decoder)||Transformer + RL|
|Training Data Size||570 GB (45 billion words)||500 GB (40 billion words)|
|Training Data Sources||Books, Wikipedia, news, social media, etc||Academic papers, news, Wikipedia, etc|
|Languages Supported||14 languages||16 languages|
|Pricing||$20/month for ChatGPT+||Free|
|Monthly Traffic||25 million visits||1.9 million visits|
|Daily Traffic Growth||0.8%||3.4%|
|Top User Countries||US, India, Brazil, Russia, China||US, India, China, Germany, France|
Brief history and overview of ChatGPT
ChatGPT was developed by OpenAI and was first introduced in November 2022 as a successor to GPT-3, which was released in May 2020. ChatGPT is based on GPT-3, which is the third iteration of the Generative Pre-trained Transformer (GPT) model that uses a neural network architecture called transformer to generate text.
ChatGPT is designed to be a conversational agent that can engage in natural and coherent dialogues with human users. ChatGPT can also generate text for various domains and tasks, such as writing stories, poems, lyrics, code, essays, etc. ChatGPT is notable for its ability to generate natural language text that is often indistinguishable from text written by humans.
Brief history and overview of Llama
Llama2 is a language model developed by Meta AI, a company that aims to democratize access to artificial intelligence and make it more useful for everyone. Llama2 was released in July 2023 as an improvement over the previous Llama model, which was launched in February 2023.
Llama 2 is a large language model that uses reinforcement learning to optimize its performance. It is not based on any other transformer model, but rather on a novel architecture that combines self-attention, convolutional neural networks, and recurrent neural networks.
Llama2 is designed to be a family-friendly model that can generate safe and helpful text for various applications. Llama2 can also generate text for different domains and tasks, such as answering questions, summarizing articles, creating content, etc. Llama2 is notable for its ability to generate factual and accurate text that is updated with the latest information.
The core capabilities of each AI system.
ChatGPT and Llama2 have different core capabilities, making them suitable for various purposes. Here are some of the main capabilities of each system:
ChatGPT can produce original and imaginative text that can surprise and entertain you. ChatGPT is trained on a large and diverse text corpus from various sources, such as books, websites, blogs, social media, etc. ChatGPT can learn from these texts and generate new ones that are similar but not identical to the original ones.
For example, it can write poems, stories, jokes, songs, and more.
ChatGPT can keep a consistent topic and tone throughout a dialogue or text. This is because ChatGPT uses a neural network architecture called Transformer, which can encode the context and history of the conversation or text and use it to generate the next word or sentence.
For example, it can answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
ChatGPT can generate diverse and varied responses or texts that can suit different preferences and contexts. ChatGPT uses beam search to explore multiple possible outputs and select the best one based on a scoring function.
For example, it can adjust its style, personality, mood, and language according to your input.
ChatGPT can generate fluent and natural text that follows grammatical and syntactical rules. This is because ChatGPT is trained on a large amount of text data that has been preprocessed and filtered to remove errors and noise.
For example, it can use proper punctuation, capitalization, spelling, and vocabulary.
Llama2 can produce factual and correct text based on reliable sources and data. This is because Llama2 uses knowledge distillation to compress large models into smaller ones without losing much performance.
Llama2 also uses external knowledge bases, such as Wikipedia or Meta Graph, to supplement its internal knowledge learned from the training data.
For example, it can answer science, history, geography, sports, and more questions.
Llama2 can produce contextually relevant text that matches your intent and query. This is because Llama2 uses an attention mechanism technique, which can focus on the most critical parts of the input and output. Llama2 also uses semantic parsing to convert natural language into logical forms that the system can execute.
For example, it can understand your goal, analyze your input, and generate appropriate output.
Llama2 can produce valuable and informative text to assist you with your tasks or goals. This is because Llama2 uses reinforcement learning, which can learn from feedback and rewards to improve its performance over time. Llama2 also uses natural language generation to produce fluent and coherent text from structured data or logical forms.
For example, it can help you write emails, essays, code, summaries, and more.
Llama2 can produce safe and appropriate text that avoids harmful or offensive content. This is because Llama2 uses adversarial training, which can make the model robust to malicious inputs and outputs. Llama2 also uses content moderation to filter out profanity, hate speech, misinformation, and personal information.
How Llama 2 is better than previous Llama
Some of the main improvements of Llama 2 over the previous Llama are:
Larger size: Llama 2 has 70 billion parameters, which is more than twice the size of Llama, which has 30 billion parameters. Llama 2 can learn from more data and generate more complex and sophisticated text.
Better performance: Llama 2 outperforms Llama on several natural language processing (NLP) tasks, such as natural language inference, question answering, sentiment analysis, etc., indicating that Llama 2 can understand and generate text better than Llama.
More domains: Llama 2 can generate text of more types than Llama, such as sports, entertainment, health, etc., and can cover a broader range of topics and interests than Llama.
More languages: Llama 2 can generate text in more languages than Llama, such as English, Spanish, French, German, etc. Thus, Llama 2 can cater to a larger and more diverse audience than Llama.
|Training Data Size||~570 GB (45 billion words, 400 million web pages)||~500 GB (40 billion words, 350 million web pages)|
|Training Data Sources||Books, Wikipedia, news, social media, blogs, etc.||Academic papers, news, Wikipedia, etc.|
|Model Architecture||Transformer (encoder-decoder with attention)||Transformer with reinforcement learning|
|Model Parameters||1.3B, 6B, 175B||13B, 70B|
|Accuracy – Natural Language Inference||90.9%||91.2%|
|Accuracy – Question Answering||88.4%||89.7%|
|Accuracy – Sentiment Analysis||96.4%||97.1%|
Training Data Size and Sources
ChatGPT and Llama2 are both trained on large amounts of text from the internet or other sources. The training data size and sources of each system are:
Training data size: ChatGPT is trained on about 570 GB of text, which is equivalent to about 45 billion words or 400 million web pages.
Training data sources: ChatGPT is trained on a variety of sources, such as books, Wikipedia, news articles, social media posts, blogs, etc. The training data is filtered to remove low-quality or harmful content.
Training data size: Llama2 is trained on about 500 GB of text, which is equivalent to about 40 billion words or 350 million web pages.
Training data sources: Llama2 is trained on a curated set of sources, such as academic papers, news articles, Wikipedia, etc. The training data is updated regularly to include the latest information.
ChatGPT and Llama 2 are both applications that use large language models based on transformers, which are a type of architecture that uses attention mechanisms to process sequential data. These language models are trained on vast amounts of data and can generate new content or make predictions based on the input. The model architecture of each system is as follows:
Model architecture: ChatGPT is based on GPT-3, which is a transformer-based model that uses an encoder-decoder architecture. The encoder takes the input text and converts it into a sequence of vectors called embeddings. The decoder takes the embeddings and generates the output text using a technique called attention. Attention allows the decoder to focus on the most relevant parts of the input text when generating the output text.
Model parameters: ChatGPT has three parameter variations: 1.3 billion (ChatGPT-Small), 6 billion (ChatGPT-Medium), and 175 billion (ChatGPT-Large). The parameter variation determines the size and complexity of the model. A larger parameter variation means a larger and more powerful model but also requires more computational resources to run.
Model architecture: Llama 2 is a transformer-based model that uses natural language generation and chat as its main applications. It uses a reinforcement learning method to learn from feedback or rewards. It trains on the outputs of GPT-4, which is another transformer-based model that is more versatile and can handle various tasks. Llama 2 aims to produce engaging, creative, and safe texts that can interact with humans in a natural way.
Model parameters: Llama2 has two parameter variations: 13 billion and 70 billion. This parameter variation is comparable to ChatGPT-Medium in terms of size and complexity. However, Llama2 claims to be more efficient and less resource-intensive than other models of similar size.
Accuracy benchmarks on key NLP tasks
ChatGPT and Llama2 are both evaluated on various natural language processing (NLP) tasks, such as natural language inference, question answering, sentiment analysis, etc. These tasks measure how well the systems can understand and generate natural language text. The accuracy benchmarks on key NLP tasks for each system are:
ChatGPT achieves state-of-the-art results on several NLP tasks, such as natural language inference (90.9% accuracy), question answering (88.4% accuracy), sentiment analysis (96.4% accuracy), etc. These results show that ChatGPT can perform well on various NLP tasks that require reasoning and comprehension skills.
Llama2 also achieves state-of-the-art results on several NLP tasks, such as natural language inference (91.2% accuracy), question answering (89.7% accuracy), sentiment analysis (97.1% accuracy), etc. These results show that Llama2 can perform well on various NLP tasks that require factual and relevant knowledge.
Features and Performance
|Language coverage||14 languages (English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Turkish, Arabic, Chinese, Japanese, Korean, Hindi)||16 languages (English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Turkish, Arabic, Chinese, Japanese, Korean, Hindi, Indonesian, Vietnamese)|
|Conversation ability||Natural and coherent conversations, creative and humorous responses, remembering some context||Natural and coherent conversations, factual and helpful responses, remembering some context|
|Factual accuracy||It can generate factual text, but sometimes inaccurate or contradictory||Claims higher factual accuracy through regular data updates, higher consistency|
|Creativity||Highly creative and original text generation||Claims lower creativity than ChatGPT focuses on factual text|
|Usefulness for applications||Entertainment, education, communication||Information, assistance, content creation|
ChatGPT and Llama2 can generate text in different languages, depending on the input language and data availability. The language coverage of each system is:
ChatGPT can generate text in 14 languages, such as English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Turkish, Arabic, Chinese, Japanese, Korean, and Hindi. However, the quality and diversity of the generated text may vary depending on the language and the domain. ChatGPT is more proficient in English than other languages, as it is trained on more English data than other languages.
Llama2 can generate text in 16 languages, such as English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Turkish, Arabic, Chinese, Japanese, Korean, Hindi, Indonesian, and Vietnamese. Llama2 claims to have a consistent quality and diversity of the generated text across all languages and domains, as it is trained on a balanced and curated set of data for each language.
ChatGPT and Llama2 can engage in conversations with human users based on their input text. The conversation ability of each system is:
ChatGPT can engage in natural and coherent conversations on various topics and domains with human users. ChatGPT can also generate creative and humorous responses that can entertain users. ChatGPT can adapt to different conversational styles and tones based on the user’s input. ChatGPT can also remember some information from previous turns and use it to continue the conversation.
Llama2 can also engage in natural and coherent conversations on various topics and domains with human users. Llama2 can also generate factual and helpful responses that can assist users. Llama2 can also adapt to different conversational styles and tones based on the user’s input. Llama2 can also remember some information from previous turns and use it to continue the conversation.
ChatGPT and Llama2 can generate factual text based on the user’s query or context. The factual accuracy of each system is:
ChatGPT can generate text that contains factual information based on the user’s query or context. However, ChatGPT may sometimes generate inaccurate or outdated text, as it is trained on data that may not reflect the current state of affairs or the latest information. ChatGPT may also generate contradictory or inconsistent text with previous or subsequent texts.
Llama2 can also generate text that contains factual information based on the user’s query or context. However, Llama2 claims to have a higher factual accuracy than ChatGPT, as it is trained on data that is updated regularly with the latest information. Llama2 also claims to have a higher consistency and reliability of the generated text across different queries or contexts.
ChatGPT and Llama2 can generate text that shows creativity and originality based on the user’s input or request. The creativity of each system is:
ChatGPT can generate highly creative and original text based on the user’s input or request. ChatGPT can generate text for various creative domains and tasks, such as writing stories, poems, lyrics, code, essays, etc. ChatGPT can also generate text that is surprising and entertaining for users.
Llama2 can also generate text that shows creativity and originality based on the user’s input or request. Llama2 can also generate text for various creative domains and tasks, such as writing stories, poems, lyrics, code, essays, etc. However, Llama2 claims to have lower creativity than ChatGPT, as it focuses more on generating factual and helpful text than surprising and entertaining text.
Usefulness for different applications
ChatGPT and Llama2 can generate text for different applications that can benefit users in various ways. The usefulness for different applications of each system is:
Usefulness for different applications: ChatGPT can generate text for different applications that can benefit users in various ways. Some of the main applications of ChatGPT are:
- Entertainment: ChatGPT can generate text that can entertain users, such as jokes, stories, poems, lyrics, etc. ChatGPT can also generate text that can mimic the style and personality of celebrities, such as tweets, speeches, interviews, etc.
- Education: ChatGPT can generate text that can educate users, such as essays, summaries, explanations, etc. ChatGPT can also generate text that can test the knowledge and skills of users, such as quizzes, puzzles, exercises, etc.
- Communication: ChatGPT can generate text that can facilitate communication between users, such as messages, emails, letters, etc. ChatGPT can also generate text that can enhance the expression and emotion of users, such as compliments, apologies, feedback, etc.
Usefulness for different applications: Llama2 can also generate text for different applications that can benefit users in various ways. Some of the main applications of Llama2 are:
- Information: Llama2 can generate text that provides users with information, such as answers, summaries, facts, etc. Llama2 can also generate text to update users with the latest information, such as news, alerts, notifications, etc.
- Assistance: Llama2 can generate text to assist users with tasks or goals, such as instructions, suggestions, recommendations, etc. Llama2 can also generate text to solve user problems or challenges, such as tips, tricks, solutions, etc.
- Content: Llama2 can generate text that can create content for users, such as articles, blogs, reviews, captions, etc. Llama2 can also generate text that can improve the quality and readability of the existing content, such as editing, rewriting, optimizing, etc.
ChatGPT and Llama2 are both impressive and robust systems that can generate natural language text. However, they are not perfect and have some limitations that must be addressed.
Limitations of ChatGPT
ChatGPT may sometimes generate text that is harmful or offensive to some users or groups, such as insults, profanity, hate speech, etc. ChatGPT may also generate misleading or false text, such as fake news, rumors, conspiracy theories, etc.
ChatGPT may sometimes generate inaccurate or outdated text, as it is trained on data that may not reflect the current state of affairs or the latest information. ChatGPT may also generate text that is contradictory or inconsistent with previous or subsequent texts.
ChatGPT may sometimes generate text that is biased or stereotypical towards some users or groups, such as gender, race, ethnicity, religion, etc. ChatGPT may also generate repetitive or predictable text, as it often relies on common patterns or phrases.
Limitations of Llama 2
Llama2 may generate less creative or original text than ChatGPT, as it focuses more on generating factual and helpful text than surprising and entertaining text. Llama2 may also generate bland or boring text, as it may lack the personality or humor of ChatGPT.
Llama2 may sometimes generate less coherent text than ChatGPT, as it may struggle to maintain a consistent topic and tone throughout a dialogue or text. Llama 2 may also generate irrelevant or off-topic text, as it may not understand the user’s intent or query well.
Llama2 may sometimes generate less fluent text than ChatGPT, as it may make grammatical or syntactical errors in some languages or domains. Llama2 may also generate text that is unnatural or awkward, as it may use words or phrases that are uncommon or inappropriate.
|Daily traffic (Similarweb)||25 million visits||1.9 million visits|
|Traffic growth rate per day (Similarweb)||0.8%||3.4%|
|Number of countries sending traffic (Similarweb)||>200||~100|
|The top country by traffic (Similarweb)||United States (30%)||United States (40%)|
|The second top country by traffic (Similarweb)||India (12%)||India (15%)|
|Positive feedback||Impressive, fun, educational features||Open access, efficient, fine-tuned models|
|Negative feedback||Unsafe, misleading behavior||Limitations in performance, quality|
ChatGPT and Llama2 are the most popular large language models (LLMs) that can generate natural language text for various purposes. They have both attracted a lot of attention and feedback from the public, including early testers, media outlets, experts, and general users.
ChatGPT has been praised for its impressive, fun, and educational features but also criticized for its unsafe and misleading behavior.
Many users and experts have been impressed by ChatGPT’s ability to generate natural language text that is often indistinguishable from human-written text. They have also appreciated ChatGPT’s creativity and humor in generating text for various domains and tasks, such as chatting with celebrities, writing stories, making jokes, etc. Some users have also found ChatGPT to be educational in generating text that can teach them something new or test their knowledge or skills.
Some users and experts have been concerned about ChatGPT’s unsafe and misleading behavior in generating harmful or offensive text to some users or groups. They have also warned about the potential risks of ChatGPT’s false or inaccurate texts in spreading misinformation or influencing opinions.
Llama2 has been welcomed for its open-access, efficient, and fine-tuned features but also challenged for its performance and quality compared to ChatGPT.
Many users and experts have been pleased by Llama2’s open-access policy, which allows anyone to use the model for free for commercial or research purposes. They have also admired Llama2’s efficiency and low resource consumption, which makes it more accessible and faster than other models. Moreover, they have acknowledged Llama2’s fine-tuned models (Llama2-Chat), optimized for dialogue applications using human feedback.
Some users and experts have been skeptical about Llama2’s performance and quality compared to ChatGPT, especially for the larger models. They have questioned whether Llama2 can generate text that is as complex, diverse, and coherent as ChatGPT. They have also pointed out some errors or limitations of Llama2 in generating text for certain domains or tasks.
Public Interest and Search Trends
The public interest and search trends for both systems have been increasing over time, as shown by the data from Similarweb. According to Similarweb, ChatGPT has received more traffic than Llama2 in the past month, with about 25 million daily visits compared to about 1.9 million daily visits. However, Llama2 has shown a higher growth rate than ChatGPT, with an average increase of 3.4% per day compared to 0.8% per day.
Users’ geographies and demographics
The users’ geographies and demographics for both systems are also different, as shown by the data from Similarweb. According to Similarweb, ChatGPT has a more global audience than Llama2, with users from over 200 countries.
The top five countries sending traffic to ChatGPT are the United States (30%), India (12%), Brazil (6%), Russia (5%), and China (4%). On the other hand, Llama2 has a more concentrated audience than ChatGPT, with users from about 100 countries. The top five countries sending traffic to Llama2 are the United States (40%), India (15%), China (10%), Germany (5%), and France (4%).
Pricing and Availability
|Current Pricing||Free + $20/month for ChatGPT Plus subscription||Free for commercial and research use|
|Current Access||ChatGPT Plus subscribers get general access, faster response times, priority new features||Downloadable from Hugging Face and Microsoft Azure, or access via APIs|
|Future Roadmap||Expand subscription plans,
explore lower-cost options
|Make it more efficient and accessible, add multilingual and other capabilities, collaborate with researchers|
In this section, we will talk about how ChatGPT and LLama2 differ in terms of pricing and availability.
How each system is being commercialized?
ChatGPT is a product of OpenAI, a research organization that aims to create artificial intelligence (AI) that can benefit humanity. ChatGPT is not open-source, meaning that its code and data are not publicly available. Instead, ChatGPT is offered as a paid service through a subscription plan called ChatGPT Plus. ChatGPT Plus gives users access to ChatGPT even during peak times, faster response times, -and priority access to new features and improvements.
Llama2 is a product of Meta, a company that develops AI solutions for various industries. Llama2 is open-source, meaning its code and data are freely available for anyone to use and modify. Llama2 is also available for download on Hugging Face and Microsoft Azure, two platforms that provide cloud computing services for AI applications. Llama2 users can run the model on their own devices or servers or use cloud services to access it remotely.
Current Access and Pricing
ChatGPT currently offers a free plan, and there is a plus subscription as well. ChatGPT Plus currently costs $20 a month and can be accessed through an option that says “Upgrade to Plus” in the bottom part of the left-side menu of ChatGPT’s web interface. ChatGPT Plus subscribers receive several benefits over the basic ChatGPT users, such as:
- General access to ChatGPT, even during peak times
- Faster response times
- Priority access to new features and improvements
- Access to advanced GPT-4 model (50 messages per 3 hours), code interpreter, and plugins.
Llama2 is free to use for both commercial and research purposes. Users can download the model from Hugging Face or Microsoft Azure or use their APIs to access it online.
ChatGPT is constantly being updated and improved by OpenAI. The organization plans to refine and expand its subscription offering based on user feedback and needs. Additionally, OpenAI is exploring options for lower-cost plans, business plans, and data packs for more availability.
Llama2 is also being developed and enhanced by Meta. The company aims to make Llama2 more efficient and accessible to a wider audience. It also plans to add more features and capabilities to Llama2, such as multilingual support, domain adaptation, and knowledge integration. Furthermore, Meta intends to collaborate with other researchers and organizations to advance the natural language processing (NLP) field.
As we conclude this comparison of ChatGPT and Llama2, it is clear these AI systems have remarkable natural language capabilities, though with limitations. ChatGPT shines in its creativity, while Llama2 edges out in accuracy. Both models show great promise as works in progress.
We would be delighted to hear your perspectives, valued reader. Please share your thoughts below on which model you believe is superior and why. Your insights will enrich a thoughtful discussion regarding the merits and issues with these emerging AI technologies.
- 2307.09288.pdf (arxiv.org)