A Pragmatic Comparative Analysis of GPT4 OpenAI (USA), Aleph Alpha’s Luminous-Supreme-Control (Germany) and the local run, open source model Llama2 by Facebook
TL;DR: In a brief comparison, we assessed the performance of three language models: GPT-4 from OpenAI (USA), Aleph Alpha’s Luminous-Supreme-Control (Germany), and the locally runnable, open-source model Llama2 by Facebook. Our evaluation indicates that GPT-4 emerged as the frontrunner, while Llama2 offers a compelling option for self-controlled use. Aleph Alpha’s model falls short in comparison.
At AGENTS.inc, we leverage the capabilities of AI to develop AI Agents that streamline essential business functions. These agents operate by connecting internal and external data sources, employing knowledge graphs and AI models to perform advanced analytics. Tasks like identifying companies, monitoring competitors, and analyzing patents and scientific documents are the daily bread and butter of our AI Agents operations.
In this context, the versatility of Large language models (LLMs) in the ever-evolving landscape of AI is undeniable. These models, such as GPT-4, have showcased remarkable performance and have the potential to transform various industries by offering versatile, out-of-the-box solutions. LLMs have taken center stage in this AI-driven transformation, offering exceptional versatility in various applications.
One burning question often lingers in the minds of our customers and the broader AI community: Where does the data go, and how is it processed? This question is especially crucial given the growing concerns about data privacy and security. While we employ our custom AI models that ensure data security and privacy, the role of LLMs, or large language models, in addressing these concerns is of growing interest. The geographic location and ownership of LLM servers impacts data privacy and compliance with regional and corporate data protection guidelines. GDPR, for instance, imposes strict regulations on data usage, storage, and transfer. IT security standards and compliance measures protect organizations from sharing data with 3rd parties.
In this article, we aim to provide a comparison of GPT-4, Luminous-Supreme-Control by Aleph Alpha, Germany’s most prominent AI startup, and Facebook’s LLAMA2 70B, the first model of this size to be open source. Our focus is to explore their versatility and performance in a variety of practical use cases. However, it’s important to note that this is not a scientifically controlled test. Rather, it’s a hands-on exploration of how these models fare in our daily use cases. The goal is to provide valuable insights to our audience and help them assess how these models could potentially benefit their specific use cases. We’ve carefully selected these three notable contenders to represent different facets of the industry:
- First, we have OpenAI’s GPT-4, widely considered the industry default and a benchmark for AI language models. GPT-4 is a powerful model with remarkable capabilities. However, it’s primarily available as a service within the United States (or as a service by Microsoft tailored for large corporations).
- Next, we’re shining a spotlight on Aleph Alpha’s Luminous-Supreme-Control (LSC). As a German startup, Aleph Alpha works in this competitive field, showcasing the innovative drive emanating from Europe. By including their model, we underscore the emerging alternatives challenging the established U.S. AI giants.
- Finally, we feature Facebook’s Llama 70B, an open-source model that, given the requisite computing infrastructure, can be run locally. The inclusion of Llama exemplifies the potential for decentralized AI solutions, where users have the autonomy to harness these powerful models without relying on external services.
In the following sections, we delve into the details of our comparison, shedding light on the strengths and limitations of these powerful AI models. The ultimate aim is to aid businesses, researchers, and individuals in choosing the right model for their unique requirements and ensure that data privacy and security remain at the forefront of the AI revolution.
So, let’s get started.
1. Analyzing Web Data
A common and essential task in the AI landscape involves the extraction and structuring of data from websites. This process often includes sorting information such as names, email addresses, company details, street addresses, zip codes, cities, countries, and phone numbers into a structured format like JSON objects. This structured data can be used for various purposes, from contact information collection to market analysis.
To shed light on the capabilities of the AI models we’re exploring, we conducted a practical test using the information from a typical website’s imprint. The imprint we used for this experiment is our own:
IMPRINT
AGENTS HQ GmbH
Otto-Suhr-Alle 18/20
10585 Berlin
Phone: +49 30 98 33 84 66
Email:
CEO: Dr. Tatjana Samsonowa-Denef
Local Court: Charlottenburg
HRB: 159659
For the purpose of this experiment, we omitted the country to evaluate the AI models’ ability to recognize this missing piece of information and, more importantly, structure the available data into a desired format. We also aimed to see how well the AI models could handle the task of recognizing specific data elements.
Results Overview
-
- GPT-4 (OpenAI): As expected, GPT-4 managed the task effectively, providing structured data in the desired format. However, as we explore other models, you’ll notice that this model sets a high benchmark in terms of data extraction.
- LSC (Aleph Alpha): LSC, on the other hand, presented an interesting dynamic. In our initial run, it did not identify the “people” section, but it correctly provided “Germany” as the country. As a German startup ourselves, we gave it another chance, and experimented with a different model version (luminous-supreme), which eventually led to the recognition of people. This model, however, performed far worse in other use cases.
- LLAMA2 (Facebook): In our test, LLAMA2 exhibited a tendency to add some additional, interesting data, a pattern that will become more evident in further analyses. It is important to note that while it offers extended information, this can sometimes lead to less focused results that require post-processing.
extract or derive all people, emails, company name, street, zip code, cities, countries, phones in a JSON object in the format {people:[],emails:[], company:[], street:[], zip:[], city:[], country:[], phones:[]}
IMPRINT
AGENTS HQ GmbH
Otto-Suhr-Alle 18/20
10585 Berlin
Phone: +49 30 98 33 84 66
Email:
CEO: Dr. Tatjana Samsonowa-Denef
Local Court: Charlottenburg
HRB: 159659
{
“people”: [“Dr. Tatjana Samsonowa-Denef”],
“emails”: [“”],
“company”: [“AGENTS HQ GmbH”],
“street”: [“Otto-Suhr-Alle 18/20”],
“zip”: [“10585”],
“city”: [“Berlin”],
“country”: [“Germany”],
“phones”: [“+49 30 98 33 84 66”]
}
{“people”: [], “emails”: [], “company”: [“IMPRINT”], “street”: [“Otto-Suhr-Alle 18/20”], “zip”: [“10585”], “city”: [“Berlin”], “country”: [“Germany”], “phones”: []}
Sure, here’s an example of how you could extract the information you need from the JSON object:
{
“people”: [
{
“name”: “Dr. Tatjana Samsonowa-Denef”,
“title”: “CEO”
}
],
“emails”: [
{
“email”: “”
}
],
“company”: {
“name”: “AGENTS HQ GmbH”,
“address”: {
“street”: “Otto-Suhr-Alle 18/20”,
“zip”: “10585”,
“city”: “Berlin”,
“country”: “Germany”
},
“phones”: [
{
“phone”: “+49 30 98 33 84 66”
}
]
}
}
Here’s a breakdown of the extracted information:
People:
Dr. Tatjana Samsonowa-Denef (CEO)
Emails:
Company:
Name: AGENTS HQ GmbH
Address:
Street: Otto-Suhr-Alle 18/20
Zip: 10585
City: Berlin
Country: Germany
Phones:
+49 30 98 33 84 66
I hope this helps! Let me know if you have any questions.
2. Summarization and Re-writing
In the realm of AI-driven content creation, summarization and re-writing are essential tasks that often serve as the backbone of AI Agent capabilities. These tasks can range from generating concise abstracts for scientific publications to summarizing lengthy articles into more digestible pieces.
In this comparative test, we evaluated how well three AI models could handle these tasks. To do so, we employed a two-fold approach: We used a popular scientific publication in the sphere of AI (Liu, Zhuang, et al. “A convnet for the 2020s.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.) as our reference and tasked the AI models with generating a two-sentence summary. For the second part of our test, we selected a historic social science article (Rittel, Horst WJ, and Melvin M. Webber. “Dilemmas in a general theory of planning.” Policy sciences 4.2 (1973): 155-169.) and sought to create a one-sentence summary suitable for use in a state-of-the-art report.
Results Overview
- GPT-4 (OpenAI): GPT-4 performed impressively, accurately generating succinct summaries in both scenarios. Its ability to comprehend and rephrase complex content highlights its potential in this domain.
- LSC (Aleph Alpha): LSC’s performance in these tasks raised some concerns. The model did not comply with the sentences limits set by the prompt. Additionally, it struggled to manage the perspective change we introduced and continued to use “we” in the first case.
- LLAMA 2 (Facebook): LLAMA 2 demonstrated robust summarization capabilities, providing concise summaries in both cases. Its proficiency in producing coherent and contextually relevant summaries is evident.
Summarize the following articles in two sentences to be used in a State-of-the-Art report.
The “Roaring 20s” of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually “”modernize”” a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
The research revisits the conventional ConvNets or convolutional neural networks design, enhancing them to compete effectively with the currently superior Vision Transformers (ViTs). The researchers established a new model called ConvNeXt, which is entirely based on ConvNet modules and performs competitively in terms of accuracy and scalability, surpassing ViTs on specific tasks like COCO detection and ADE20K segmentation whilst preserving the simplicity and efficiency of standard ConvNets.
The “Roaring 20s” of computer vision was marked by the introduction of ViTs, which quickly surpassed ConvNets as the state-of-the-art image classification model. However, pure ViTs are not well-suited for general computer vision tasks such as object detection and semantic segmentation. In this work, we explore the design spaces of pure ConvNets and discover several key components that contribute to their performance. The outcome is a family of ConvNet models, dubbed ConvNeXt, that achieve state-of-the-art performance on a variety of vision tasks.
The article discusses the resurgence of convolutional neural networks (ConvNets) as a viable alternative to vision transformers (ViTs) for image classification tasks, and presents a family of pure ConvNet models called ConvNeXt that competes favorably with Transformers in terms of accuracy and scalability. The authors argue that the effectiveness of hybrid approaches that combine ConvNets and Transformers is due to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions.
Summarize the following abstract in one sentence to be used in a State-of-the-Art report.
The search for scientific bases for confronting problems of social policy is bound to fail, becuase of the nature of these problems. They are “wicked” problems, whereas science has developed to deal with “tame” problems. Policy problems cannot be definitively described. Moreover, in a pluralistic society there is nothing like the undisputable public good; there is no objective definition of equity; policies that respond to social problems cannot be meaningfully correct or false; and it makes no sense to talk about “optimal solutions” to social problems unless severe qualifications are imposed first. Even worse, there are no “solutions” in the sense of definitive and objective answers.
The search for scientific bases for confronting problems of social policy is unlikely to succeed due to the inherent nature of these problems, which are “wicked” and cannot be definitively described, and the lack of an objective definition of equity and optimal solutions in a pluralistic society.
The author of this abstract is arguing that social policy problems are “wicked” problems, and that science is not the appropriate tool to deal with them. The author is also arguing that there is no objective definition of equity, and that there is no objective definition of “optimal solutions” to social problems.
The abstract argues that the use of scientific bases to address social policy problems is futile due to the complex, “wicked” nature of these issues, which cannot be definitively described or solved, particularly in a pluralistic society where concepts like public good and equity lack objective definitions.
3. COMPANY DATA COMPARISON
Comparing companies plays a vital role in various AI Agent tasks, from identifying potential suppliers to pinpointing M&A targets. The AI model’s ability to process uncleaned, mixed-language text from websites and generate accurate company descriptions is paramount for automating this process.
In our comparison, we provided the AI models with unedited text from websites related to companies in the metal processing industry. We also included text from a yellow pages source, which introduced our AI Agents’ common challenge of irrelevant results.
RESULTS OVERVIEW
- GPT-4 (OpenAI): GPT-4 exhibited an impressive performance. It effectively clustered the data into two distinct groups.
- LSC (Aleph Alpha): Aleph Alpha’s model performance was the least satisfactory in this comparison. The model hallucinated and failed to abstract data accurately, which is a critical requirement for AI Agents to process and interpret information correctly.
- LLAMA2 (Facebook): While LLAMA2 generated detailed company keywords, it struggled with the grouping task, failing to categorize the companies effectively.
Group the following companies by what they do.
Company A. ÜBER UNS. Auf unseren modernsten Blechverarbeitungsmaschinen fertigen wir Teile in Lohnarbeit sowie diverse Eigenprodukte für die Landwirtschaft. Wir sind ein führender Betrieb für Blechbearbeitung in der Schweiz. Planung, Konstruktion, Fabrikation und Montage aus einer Hand bietet Garantie für einwandfreie fachkundige Arbeiten. .Blechbearbeitung mit Schweizer üá®üá≠ Pr√§zision ‚úì Zuschneiden ‚úì Umformen ‚úì Veredeln & Verarbeiten ‚úì 3D Bearbeitung ‚úì An 4 Standorten in der Schweiz. TECHNOLOGIE Maschinenliste Dank Winkelsensorik biegen wir genauer CNC-Abkanten bis 6 m, dank automatischer Winkelsensorik-Korrektur bieten wir Ihnen höchste Präzision. Wir verpassen den letzten Schliff Maschinelle Blechentgratung mit Oberflächen-Finish für noch prachtvollere Werkstücke. Sparen Sie sich mühsames Nachzeichnen Falls Sie keine.
Company B. Rufen Sie uns an oder schreiben Sie eine E-Mail. Die Bearbeitung von Großteilen, wie auch von Kleinteilen ist mit Hilfe unseres Maschinenparks kein Problem. CNC-Fräsen Großteile. CNC-Fräsen Kleinteile. CNC-Drehen. Lackieren. Schweißen. Sandstrahlen. Wir beantworten Ihre Fragen gerne und unverbindlich. .Hermann Maschinenbau
Company C. Homepage – Präzisionstechnik: Dreherei ; Präzisionstechnik für die Industrie Hydraulik, Pneumatik, Sensortechnik, Antriebstechnik und Automobilindustrie. Jahrzehntelange Erfahrung.. Your consultant and companion in precision engineering – for over 50 years. That we master turning, milling and grinding of high-quality CNC parts right down to the finest detail is a matter of course for our customers and ourselves. But much more awaits you at Kaiser: As a 2nd generation owner-managed company with over 50 […]
Company D. Gelbe Seiten – Das Branchenbuch für Deutschland gibt Auskunft zu Telefonnummern, Adressen, Faxnummern und Firmen-Infos in den Einträgen der Unternehmen nach Branchen .Gelbe Seiten – Das Branchenbuch für Deutschland gibt Auskunft zu Telefonnummern, Adressen, Faxnummern und Firmen-Infos in den Einträgen der Unternehmen nach Branchen.
Company Group 1 (Metalworking and machinery): Company A, Company B, Company C
Company Group 2 (Directory services): Company D
A. Blechbearbeitung in der Schweiz. Planung, Konstruktion, Fabrikation und Montage aus einer Hand.
B. Hermann Maschinenbau.
C. Präzisionstechnik: Dreherei ; Präzisionstechnik für die Industrie Hydraulik, Pneumatik, Sensortechnik, Antriebstechnik und Automobilindustrie.
D. Gelbe Seiten – Das Branchenbuch für Deutschland.
Company A: Blechbearbeitung (Sheet metal working)
Company B: Maschinenbau (Mechanical engineering)
Company C: Präzisionstechnik (Precision engineering)
Company D: Branchenbuch (Yellow pages)
4. Sentiment analysis
In our evaluation, we conducted sentiment analysis on the sentence: “Petrobras vence no STF e condenação trabalhista bilionária é anulada.” which translates to “Petrobras wins in the STF [Federal Supreme Court in Brazil] and a billion-dollar labor conviction is annulled.”. This sentence is particularly interesting due to its dual nature, encompassing the idea of “winning” alongside “annulment.” In a classical sentiment analysis, that only judges words without context, this combination might yield contradictory outcomes.
RESULTS OVERVIEW
- GPT-4 (OpenAI): GPT-4 delivered a flawless sentiment rating, accompanied by a comprehensive explanation.
- LSC (Aleph Alpha): This model provided a sentiment score but didn’t furnish an explanation. We also translated the sentence to English since Aleph Alpha isn’t trained on Portuguese; however, this didn’t affect the result.
- LLAMA2 (Facebook): While arriving at a different sentiment result, displayed a sophisticated interpretation, highlighting the conflict between the notions of winning in court and labor issues.
Evaluate the following sentence with a sentiment for Petrobras, on a scale from +1.00 to -1.00 and explain your decision.
Petrobras vence no STF e condenação trabalhista bilionária é anulada.
Explanation: This sentence is universally positive for Petrobras as they won in the Supreme Federal Court and a billion-dollar labor lawsuit was annulled. This is great news for the company as it implies financial savings and successful litigation. Therefore, the sentiment is rated at +1.00.
+1,00
The model is not trained on Portuguese text. We also translated the sentence to English and changed the prompt. There was no difference in the result.
The sentence “Petrobras vence no STF e condenação trabalhista bilionária é anulada” can be translated to “Petrobras wins at the Supreme Court and billion-dollar labor conviction is annulled.”Based on this sentence, I would rate the sentiment towards Petrobras as +0.50.
The fact that Petrobras won a court case and had a labor conviction annulled suggests that the company has been successful in defending itself against legal challenges. This could be seen as a positive development for the company and its stakeholders.
However, it’s worth noting that the use of the word “trabalhista” (labor) in the sentence could suggest that the court case was related to labor disputes or worker’s rights, which could potentially be a negative issue for Petrobras.Overall, while the sentence suggests a positive outcome for Petrobras, the context and potential implications of the court case could temper the sentiment slightly, resulting in a score of +0.50.
5. TEXT GENERATION
One of the primary functions of large language AI models is text generation. These models are used for generating articles, marketing materials, social media content, and more. The effectiveness of text generation is a key consideration for any organization looking to leverage AI agents.
In our evaluation, we aimed to assess their performance in generating articles based on a detailed prompt. The quality of text generation can significantly impact the utility and relevance of AI-generated content. As you can see, we used the prompt to write the initial introduction of this very article.
RESULTS OVERVIEW
- GPT-4 (OpenAI): GPT-4 produced an article that was not only comprehensive but also engaging and interesting. The generated text was coherent and well-structured. Additionally, GPT-4 performed well in the creation of catchy LinkedIn posts to introduce the article.
- LSC (Aleph Alpha): When evaluating the performance of Aleph Alpha in text generation, it became evident that the model has some distinct limitations. One notable challenge we encountered was its ability to generate useful content for introductory purposes. In our assessment, Aleph Alpha failed to produce text suitable for introductions. The model’s ability to process a restricted number of words (up to 2048 tokens) posed a challenge in summarization of the long text. However, when we presented the model with shorter input versions, it demonstrated its potential by generating the most attention-grabbing and engaging slogans in our evaluation.
- LLAMA 2 (Facebook): LLAMA 2 generated an article that was on par with GPT-4 in terms of interest and readability. The text was engaging and logically organized, making it suitable for a wide range of applications. Furthermore, LLAMA 2 excelled in understanding the catchiness for LinkedIn posts.
Write me the first part of the article before starting the comparison.
I am writing comparing different large language models: OpenAI’s GPT4, Aleph Alpha’s Luminous-Supreme and Facebook’s LLAMA 70B. The test is to compare versatility of use cases in our AI Agents. Our agents use internal and external data sources, knowledge graphs and AI models to perform analytics. Finding companies, monitoring competitors, analyze patents and scientific documents. Often our customers wonder where agent send and process data. While we use our own and custom AI models, the use of LLMs as a universal solutions becomes more and more relevant, given their extraordinary performance. Our results show that the performance of GPT4 is not matched by Aleph Alphas model. Interestingly, the open source LLAMA2 version provides a viable alternative where we can provide companies with a solution that does not send data to a 3rd party provider. We will show the details of the comparison in the following. This is not to be confused to be a scientific test, rather we test our own daily use cases and others should check how the models perform in their use cases.
As artificial intelligence becomes more integrated in our daily lives, the importance of large-scale language models (LLMs) cannot be overstated. These machine learning models are powering everything from virtual personal assistants, chatbots, and search engines, to systems for information extraction, machine translation, and even composition of long form text. This makes the testing of these models a high-stakes endeavor, one that can drastically affect the quality and efficiency of numerous AI-powered services and systems.
The recent years have seen major technology companies entering the fray, launching advanced language models. Giants like OpenAI, Aleph Alpha, and Facebook AI Research have unveiled their language models, making the landscape increasingly competitive. This also presents an opportunity for consumers of AI solutions to evaluate the models in terms of performance, security and versatility in application.
In this article, we feature three heavyweights in the field: GPT4 from OpenAI, Luminous-Supreme from Aleph Alpha, and LLAMA 70B, developed by Facebook. Our focus is on evaluating these language models’ versatility across various use cases with AI agents that use internal and external data sources, knowledge graphs and AI models to perform analytic tasks.
These models help us in identifying companies, keeping tabs on competitors, analyzing patents and scientific documents to pick a few. Data is at the heart of our operations, and a question that is of concern to many of our customers is: “”where is our data being sent, and how is it being processes?”” While we utilize custom AI models along with our own, the shift towards using LLMs as ‘universal solutions’ has become an appealing prospect due to their superlative performance.
Findings from preliminary tests indicate unmatched performance by OpenAI’s GPT4 when compared with Aleph Alpha’s model. The results, however, introduce another contender into the mix. LLAMA2, the open-source variant of LLAMA, opens possibilities for providing a solution that doesn’t necessarily send data to a third-party provider.
Before we delve deeper into the comparison of these models, it is important to clarify that our testing is not scientific in nature, but is based on our daily use cases. Therefore, we encourage other interested parties to perform similar tests considering their specific use cases. Let’s unpack this comparison in detail.
We are testing different large language models for our AI Agents. LLMs are becoming more and more relevant. We will show the results of our tests.
The advent of large language models (LLMs) has revolutionized the field of natural language processing and has had a significant impact on various industries. At our company, we specialize in developing AI agents that use internal and external data sources, knowledge graphs, and AI models to perform analytics for our clients. Our agents are designed to perform a wide range of tasks, including finding companies, monitoring competitors, analyzing patents, and scientific documents.
One of the common questions our customers ask is where our agents send and process data. While we use our own custom AI models, the use of LLMs as a universal solution has become increasingly relevant due to their extraordinary performance. In this article, we will compare three popular LLMs – OpenAI’s GPT4, Aleph Alpha’s Luminous-Supreme, and Facebook’s LLAMA 70B – to determine their versatility in our AI agents.
Our comparison is not meant to be a scientific test, but rather a practical evaluation of how these models perform in our daily use cases. We will share the details of our comparison in the following sections, and we encourage others to test these models in their own use cases to see how they fare.
The models we are comparing are:
- OpenAI’s GPT4: A powerful LLM developed by OpenAI, a leading AI research organization. GPT4 is known for its impressive language generation capabilities and has been used in a variety of applications, including chatbots, language translation, and text summarization.
- Aleph Alpha’s Luminous-Supreme: A highly advanced LLM developed by Aleph Alpha, a company known for its cutting-edge AI solutions. Luminous-Supreme is designed to handle complex tasks and has been used in various industries, including finance, healthcare, and retail.
- Facebook’s LLAMA 70B: A popular LLM developed by Facebook, a leading social media platform. LLAMA 70B is known for its ability to handle conversational AI and has been used in various applications, including chatbots, virtual assistants, and language translation.
In the following sections, we will provide a detailed comparison of these models, highlighting their strengths and weaknesses, and discussing their suitability for various use cases. We will also discuss the advantages and disadvantages of using LLMs in our AI agents and provide recommendations for businesses looking to incorporate these models into their operations.
Write me a catchy linkedin abstract for this article
As artificial intelligence becomes more integrated in our daily lives, the importance of large-scale language models (LLMs) cannot be overstated. These machine learning models are powering everything from virtual personal assistants, chatbots, and search engines, to systems for information extraction, machine translation, and even composition of long form text. This makes the testing of these models a high-stakes endeavor, one that can drastically affect the quality and efficiency of numerous AI-powered services and systems.
The recent years have seen major technology companies entering the fray, launching advanced language models. Giants like OpenAI, Aleph Alpha, and Facebook AI Research have unveiled their language models, making the landscape increasingly competitive. This also presents an opportunity for consumers of AI solutions to evaluate the models in terms of performance, security and versatility in application.
In this article, we feature three heavyweights in the field: GPT4 from OpenAI, Luminous-Supreme from Aleph Alpha, and LLAMA 70B, developed by Facebook. Our focus is on evaluating these language models’ versatility across various use cases with AI agents that use internal and external data sources, knowledge graphs and AI models to perform analytic tasks.
These models help us in identifying companies, keeping tabs on competitors, analyzing patents and scientific documents to pick a few. Data is at the heart of our operations, and a question that is of concern to many of our customers is: “where is our data being sent, and how is it being processes?” While we utilize custom AI models along with our own, the shift towards using LLMs as ‘universal solutions’ has become an appealing prospect due to their superlative performance.
Findings from preliminary tests indicate unmatched performance by OpenAI’s GPT4 when compared with Aleph Alpha’s model. The results, however, introduce another contender into the mix. LLAMA2, the open-source variant of LLAMA, opens possibilities for providing a solution that doesn’t necessarily send data to a third-party provider.
Before we delve deeper into the comparison of these models, it is important to clarify that our testing is not scientific in nature, but is based on our daily use cases. Therefore, we encourage other interested parties to perform similar tests considering their specific use cases. Let’s unpack this comparison in detail.
Unlocking language models’ potential in AI – Analysing the performances of top contenders GPT4, Luminous-Supreme and LLAMA 70B. Learn how these AI giants are shaping and transforming the landscape of artificial intelligence and the future of data security. Delve into our industry-based evaluation, not scientific but based on practical use cases as we steer towards LLMs as ‘universal solutions’. Click to explore more.
The model cannot handle the large prompt. We gave it shorter article to test:
In this article, we feature three heavyweights in the field: GPT4 from OpenAI, Luminous-Supreme from Aleph Alpha, and LLAMA 70B, developed by Facebook. Our focus is on evaluating these language models’ versatility across various use cases with AI agents that use internal and external data sources, knowledge graphs and AI models to perform analytic tasks. Findings from preliminary tests indicate unmatched performance by OpenAI’s GPT4 when compared with Aleph Alpha’s model. The results, however, introduce another contender into the mix. LLAMA2, the open-source variant of LLAMA, opens possibilities for providing a solution that doesn’t necessarily send data to a third-party provider.
Catchy LinkedIn Abstract:
- GPT4, Luminous-Supreme, and LLAMA 70B: Which AI Model is the Most Versatile?
- Preliminary Findings Suggest GPT4 Leads the Pack, but LLAMA2 Offers a Different Approach
- Unparalleled Performance by OpenAI’s GPT4, but What About Data Privacy?
Exploring the Versatility of Large-Scale Language Models: A Comparison of GPT4, Luminous-Supreme, and LLAMA 70B
What we learned
In this assessment, we delved into the intriguing world of large language AI models, specifically GPT-4, LLAMA 2, and Aleph Alpha. These models have been instrumental in transforming how we generate and interact with text, making their performance an essential aspect for AI Agents.
GPT-4 demonstrated its prowess by producing an article that not only embraced comprehensiveness but also captivated with its engaging and well-structured narrative. The generated text was not just informative but engaging, effectively showcasing its suitability for a wide range of applications. Furthermore, GPT-4 didn’t stop at article generation; it skillfully crafted catchy LinkedIn posts to introduce the content, a testament to its versatility.
LLAMA2 proved to be a formidable contender, standing toe-to-toe with GPT-4. Its generated article matched GPT-4 in interest and readability, boasting an engaging and logically organized composition. LLAMA 2’s performance in crafting catchy LinkedIn posts was noteworthy, underlining its understanding of the dynamics required for this platform.
However, when we examined Aleph Alpha’s LSC performance, it revealed some distinct limitations. Its struggle to create suitable content was a notable challenge, which makes it hard to confidently perform more complex tasks. We also observed a constraint in the model’s ability to process an extensive number of words, restricting its capabilities.
Conclusion
The scope of our evaluation is undeniably limited, but the insights gained from these experiments definitely echo our broader experience with these language models. OpenAI’s GPT-4 unquestionably stands as a dominant force in the field, consistently delivering exceptional results across various tasks. However, what makes this assessment all the more fascinating is the emergence of viable alternatives. Llama2, in particular, stands out as a compelling option, proving its power in multiple aspects. Meanwhile, Aleph Alpha, has some ground to cover to match the prowess of its competitors.
Yet, before you eagerly rush to install Llama2 on your laptop, it’s essential to bear in mind a few practical considerations. While the 7B version runs on devices like the M1 MacBook Air, the 70B version demands a formidable GPU. This requirement not only translates into significant costs but also necessitates a substantial power supply. Furthermore, providing these models at scale for swift processing poses a more complex challenge.
Adding another layer of intrigue to this landscape, the future of Llama finds itself resting in the hands of Mark Zuckerberg and his brainchild, Facebook. A recent interview with Lex Friedman indicates the uncertainty surrounding LLAMA2’s developmental trajectory and the question of whether future versions will remain open source. As of now, the race to advance AI models is fervent, with numerous contenders vying to enrich the ecosystem. This proliferation of models ultimately promises an abundance of choices for users.
In this rapidly evolving field, one thing remains abundantly clear: the realm of large language AI models is continually expanding, offering users a growing array of tools and options. The performance and feasibility of these models, such as GPT-4, Llama, and LSC, not only demonstrate the cutting-edge progress but also underscore the complexity and practicality of integrating these technologies into everyday applications. While GPT-4 holds its stronghold, LLAMA2 shines as a potent challenger.
As we navigate this dynamic landscape, it’s imperative to keep a watchful eye on the practical constraints of these models. The era of large language AI models has arrived, bringing with it not only remarkable capabilities but also an intricate web of considerations and opportunities that define the AI landscape of the future.
Disclaimer: Following the practical AI model comparison and our interpretation of the results, we tasked AI models with transforming rough content descriptions into coherent text. In essence, every word you’re reading right now was conceived by a human and then refined into polished English by AI models, underscoring the seamless collaboration between human ingenuity and artificial intelligence.
