Large language models (LLMs) are the most common type of text-handling AI, and they're showing up everywhere. ChatGPT is the most well-known LLM tool, and it is driven by a specially modified version of OpenAI's GPT models. However, LLMs are used by a wide range of chatbots and text generators, including Google Gemini and Anthropic's Claude, as well as WriteSonic and Jasper.

LLMs have been simmering in research facilities since the late 2010s, but with the publication of ChatGPT (which demonstrated the potential of GPT), they've emerged from the lab and into the real world.

Some LLMs have been under development for years. Others have swiftly emerged to capitalise on the new buzz cycle. Many more are open research tools. The first generations of large multimodal models (LMMs), which can handle different input and output modalities such as pictures, audio, video, and text, are gradually becoming widely accessible, complicating matters even more. So here, I'll go over some of the most important LLMs on the market right now.

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What are Large Language Models and How Do They Work?

Large language models are powerful AI systems capable of comprehending and producing human language. They are created utilising complicated neural network topologies, such as transformer models, which are inspired by the human brain.

These models are trained on massive volumes of data, allowing them to understand context and provide coherent text-based outputs, such as answering a question or writing a story.

Simply described, a large language model is a highly advanced generative AI that understands and generates human language. This breakthrough is changing the way we interact with computers and technology.

Large language models (LLMs), such as GPT, use a transformer architecture that uses attention mechanisms to interpret and produce human language. By breaking down text into tokens and processing them via numerous layers, these models learn patterns and word associations, allowing them to generate coherent and contextually appropriate content. To learn language patterns and facts, LLMs are first pre-trained on large datasets by predicting the next word in a series. Then they are fine-tuned on specific tasks using labelled data, tailoring their performance to tasks such as translation or summarisation.

Use Cases for LLMs

Given how fresh this is, we're still trying to figure out what's feasible with the technology. However, LLMs' capabilities are undeniably impressive, with several potential business applications. These include use as chatbots in customer service settings, code production for developers and maybe corporate users, audio transcription summarising and paraphrasing, translation, and content creation.

For example, a well trained LLM may transcribe and summarise client conversations in near real time, with the findings shared with the sales, marketing, and product teams. Or an organisation's online pages may be automatically translated into many languages. In both circumstances, the findings would be imprecise, but could be promptly evaluated and corrected by a human reviewer if necessary.

In the coding context, several prominent internal development environments now feature some sort of AI-powered code completion, with GitHub Copilot and Amazon CodeWhisperer being prime examples. Other similar applications, such as natural language database querying, have shown promise. LLMs may also be able to create developer documentation from source code.

Leading LLM Models in 2024

There are dozens of major LLMs, as well as hundreds that are deemed noteworthy for various reasons. Listing them all would be practically difficult, and in any case, it would be out of date in a matter of days due to the rapid development of LLMs.

When comparing large language models for your organisation, it is critical to understand each tool's developer, parameters, accessibility, and beginning pricing.

A note on parameters: While larger parameter sizes often indicate better LLM accuracy, keep in mind that most of these AI tools can be fine-tuned using your own company-, task-, and industry-specific data. Furthermore, several AI businesses provide many LLM models of varying sizes, with lower-parameter versions priced cheaper.

GPT 4

OpenAI's GPT-4, which is commonly accessed via the free AI program ChatGPT, is an advanced natural language processing model. Compared to other LLMs, GPT's mix of large-scale pretraining, contextual understanding, fine-tuning capabilities, and advanced design allows it to write lengthy, complex replies to your queries, making it an excellent marketing assistant.

By training GPT on your brand's tone and style, you can have it create writing that is consistent with your style and can be easily included into email campaigns, ad copy, social media posts, presentations, and other external and internal material for your company. With its new picture reader, you can even input an ad image and have it generate a creative caption.

Pros

• ChatGPT offers a free basic edition, making it accessible to a wide spectrum of users.
• ChatGPT can analyse and generate visual information, expanding its possibilities beyond text-based interactions.
• The model produces coherent and detailed text, making it useful for a wide range of activities such as writing help, brainstorming, and idea development.

Cons

• In certain situations, ChatGPT may provide illogical or irrelevant answers to the input, resulting in hallucinations or inaccuracy.
• Because of the nature of the training data, ChatGPT's output may occasionally display political bias, potentially impacting the neutrality of its answers.
• Access to sophisticated features may require a membership, restricting some functionality to paying customers.

Pricing

ChatGPT-3.5: Free version.

ChatGPT-4 Plus:  $20 per month and includes the ability to develop customised chatbots, access the latest improvements, generate images, and provide generally more intelligent replies.

Features

• Write persuasive, innovative text.
• Edit and optimise copy.
• Summarise the text and visuals. 
• Conduct market analysis.
• Do keyword research.
• Write code
• 175 billion parameters.

Falcon

Technology Innovation Institute's Falcon, which is mostly accessible through Hugging Face, is an open-source LLM designed for conversational engagements with natural back-and-forth exchanges.

Falcon is trained on conversations and social media debates, so it understands conversational flow and context, allowing it to provide highly relevant replies that take into consideration what you've previously said. In essence, the more you engage with Falcon, the more it "knows you," and the more you may benefit from it.

Falcon's artificial intelligence learning capabilities makes it suitable for AI chatbots and virtual AI assistants, which offer a more engaging, human-like experience than ChatGPT.

Pros

• Users can use the model for both commercial and research reasons, allowing for applications in a variety of industries including customer service, content development, and academic research.
• The concept offers a highly conversational user experience, allowing users to engage in natural and fluid interactions. This makes it ideal for chatbots, virtual assistants, and other interactive applications.
• The model's text closely mimics human language, making it appropriate for tasks such as content development, narrative, and conversation generation. This improves the overall quality of the user interactions.

Cons

• In comparison to other models such as GPT, this model has fewer parameters, which may restrict its capacity to capture complex language patterns and subtleties, potentially impacting output quality.
• The model only supports a small number of languages, limiting its utility for customers who require support for languages other than those listed.
• Falcon 180B requires substantial processing power and memory to execute, which may provide issues for users with restricted resources or budget limitations.

Pricing

Falcon is a free artificial intelligence tool that may be implemented into apps and end-user products.

Features

• Produce human-like textual answers.
• Track the context of the ongoing debate.
• Fine-tuned basic model
• Answer tough questions.
• Translate text, summarise information, and integrate it into your business apps for free.

Llama 2

Meta AI's Llama 2 is an open-source large language model that can help with a wide range of commercial operations, including content generation and AI chatbot training. Its three models are trained on a minimal number of parameters, resulting in quick processing and reaction time.

It is designed to be fine-tuned using your company and industry-specific data, and users may download it for free on their desktop and adapt it to their needs without needing a lot of computing resources. This makes it an excellent choice for small businesses seeking a free and adaptable LLM that is simple to implement.

Pros

• The model is rapid and efficient, making it ideal for activities that need speed. It uses fewer processing resources than some other models.
•  Because the model is open-source, users may access it for free and adapt it to meet their needs, encouraging community participation and creativity.
• The model performs well in reasoning and coding tests, making it useful for activities that need logical thinking and problem-solving abilities.

Cons 

• Without fine-tuning, the model may lack particular support for coding or mathematical activities, limiting its usefulness in some technical applications.
• While the model performs well in reasoning and coding, its output may be less creative or diversified than that of bigger models like GPT, thereby restricting its applicability for creative writing jobs.
• The model contains fewer parameters than comparable tools, which may limit its capacity to capture complicated language patterns and subtleties, thus reducing output quality.

Pricing 

Open source and free for both research and commercial use.

Features

• Advanced reading comprehension.
• Text generation
• Company-wide search engines
• Text auto-complete
• Data analysis.

Cohere

Cohere is an open weights LLM and enterprise AI platform that is popular with major corporations and international organisations looking to build a contextual search engine for their private data.

Cohere's powerful semantic analysis enables businesses to securely give it corporate information—sales data, call transcripts, emails, and so on—and then use a fast search to get answers to questions like “What were Q4 margins in the Western US?”

This simplifies intelligence collection and data analysis tasks, allowing your team to fully utilise the corporate data you collect. Cohere may be accessed using their API or Amazon SageMaker. Companies may deploy Cohere's models openly on AWS, GCP, OCI, Azure, and Nvidia, as well as via VPC or in their own on-premise environments.

Pros

• The model performs precise and insightful semantic analysis, allowing users to derive meaningful insights from text data.
• Users' data and search queries are kept secret, maintaining confidentiality and security, which is critical when dealing with sensitive information.
• The model enables a significant degree of customization, allowing users to adjust it to their individual needs and tastes, boosting its versatility and efficacy.

Cons

• The model's cost of usage is greater than that of many other large language models (LLMs), which may limit its accessibility for users on a tight budget.
• The free edition is generally used as a trial or testing ground, with restricted capability or access to advanced features that may not completely suit consumers' long-term requirements.
• Because of its greater cost and potentially difficult customization possibilities, the approach may be less appropriate for smaller enterprises and startups with limited resources or experience in natural language processing.

Pricing

• There is a free version and a Production tier, which includes three products (command, rerank, and embed) and costs per 1 million tokens of data output and input.
• Must contact Sales for a price on their highly customisable Enterprise tier.

Features

• Designed for Enterprise applications.
• Semantic analysis and context search
• Content production, summary, and classification
• Supports more than 100 languages.
• Advanced data retrieval (re-ranking).
• Deployment to any cloud or on-premise

Gemini

Gemini is a huge language model, content generator, and AI chatbot from Google's Gemini AI package. It is multimodal, which means it understands not just text but also video, code, and picture data.

Its key distinction is "Gemini for Google Workspace," an AI assistant that integrates with Google Docs, Sheets, Gmail, and Slides, offering up a plethora of use cases for Google Suite customers. Starting at $20 per month, Gemini allows you to conveniently search and generate documents, analyse spreadsheet data, write personalised emails, create presentations and decks, and more.

Pros

• The tool is cost-effective for professionals, providing essential features at a reasonable price, making it acceptable for individuals or small enterprises with limited budgets.
• Integration with Google apps improves ease and productivity by allowing users to use familiar tools and workflows.
• The tool has strong reasoning capabilities, allowing users to successfully examine and grasp complicated material, which is useful for activities that need critical thinking and problem solving.

Cons

• The accuracy of the free version, Gemini Pro, may be lower than expected, thereby restricting its utility for those that require exact results for their job.
• Because it is a newer product on the market, it may lack some features or maturity when compared to more established alternatives, affecting its dependability and adoption rate.
• Picture interactions may have faults or inconsistencies, hurting the user experience and perhaps leading to annoyance or workflow delays.

Pricing

• Provides a free version of Gemini AI with basic features.
• Gemini advanced, the Premium tier, costs $19.99 per month (which includes access to Gemini 1.0 Ultra, sophisticated Google Suite tools, and functionality for doing difficult tasks).

Features

• Conversational AI chatbot
• Create presentations easily.
• Generate content
• Analyse vast amounts of data.
• Multimodality
• Google Workspace AI Assistant

Code Snippets

Here are code snippets in Python that make use of the corresponding APIs or frameworks for each of the LLMs (GPT-4, Falcon, Llama 2, Cohere, and Gemini) to demonstrate how to communicate with them practically. These examples are predicated on your having the required libraries installed as well as the proper access rights or API credentials.

GPT-4

import openai
# Replace 'your-api-key' with your actual OpenAI API key
openai.api_key = 'your-api-key'

response = openai.Completion.create(
    engine="text-davinci-004",
    prompt="Write a creative ad copy for a new smartwatch.",
    max_tokens=100
)
print(response.choices[0].text.strip())

Falcon

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "tiiuae/falcon-180B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
inputs = tokenizer("What is the meaning of life?", return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Llama 2

from transformers import LlamaTokenizer, LlamaForCausalLM

# Load model and tokenizer
model_name = "meta-llama/Llama-2-70B"
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name)

# Generate a response
inputs = tokenizer("How can small businesses benefit from AI?", return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Cohere

import cohere

# Replace 'your-api-key' with your actual Cohere API key
co = cohere.Client('your-api-key')

response = co.generate(
    model='xlarge',
    prompt='Describe the future of artificial intelligence in healthcare.',
    max_tokens=100
)

print(response.generations[0].text.strip())

Gemini

// This script needs to be run in the Google Apps Script environment

function createDocumentWithGemini() {
  var doc = DocumentApp.create('AI Generated Document');
  var body = doc.getBody();
  
  // Example prompt for Gemini
  var prompt = "Generate an executive summary for the company's Q1 performance.";
  
  // Assuming you have an API or method to call Gemini
  var geminiResponse = callGeminiAPI(prompt);
  
  body.appendParagraph(geminiResponse);
}

function callGeminiAPI(prompt) {
  // This is a placeholder function. Replace with actual API call to Gemini
  var response = UrlFetchApp.fetch('https://gemini.api.endpoint', {
    method: 'post',
    payload: JSON.stringify({prompt: prompt}),
    headers: {'Authorization': 'Bearer your-api-token'}
  });
  
  var jsonResponse = JSON.parse(response.getContentText());
  return jsonResponse.result;
}

How to Choose the Best LLM for Your Business

The finest large language models often include fast content production, text summarization, data analysis, and third-party integrations, as well as high levels of customization and accuracy. However, the optimal large language model software for your organisation is one that meets your specific requirements, budget, and resources. Before assessing LLMs, you should determine the use cases that are most important to you, so that you could find models tailored to those applications. Do you prioritise affordability? Do you require a comprehensive feature list and the funds to implement it? Given the complexities of LLMs, notably how quickly the industry develops, extensive research is always necessary.

OWASP's Top 10 for LLMs

The OWASP Top 10 for LLMs initiative seeks to raise awareness among developers, designers, architects, managers, and entire companies about the security concerns involved with installing and maintaining LLMs.

This research identifies the top 10 important vulnerabilities, stressing their potential effect, ease of exploitation, and ubiquity in real-world systems. OWASP's primary objectives are to promote awareness of these vulnerabilities, prevent them when feasible, suggest remedial solutions, and, eventually, improve the security of all LLM applications.

Vulnerabilities and Prevention Strategies for LLMs

1 - Prompt Injection

This vulnerability involves influencing LLMs by giving misleading inputs, resulting in unexpected effects. These vulnerabilities may typically be classified into two types. Direct injections replace system prompts, whereas indirect injections change inputs from external sources.

To avoid prompt injections, it is critical to build privilege restrictions that limit access to certain activities depending on user role. The procedure should also include user approval for privileged activities. It is recommended to segregate user prompts and establish systems that visibly show any erroneous replies.

2 - Insecure Output Handling

Backend systems can be compromised if LLM output is received without sufficient validation. This omission has major repercussions, including cross-site scripting (XSS), cross-site request forgery (CSRF), server-side request forgery (SSRF), privilege escalation, and remote code execution.

Adopting a zero-trust attitude to LLM output is critical. This entails rigorous validation and sanitization of the output. Encoding LLM outputs to prevent code execution via Markdown or JavaScript is critical.

3 - Training Data Positioning

When training data for LLMs is tampered with, biases and weaknesses might emerge, jeopardising security, ethical conduct, and even the LLM's efficacy.

To avoid manipulation of training data, all external data sources should be checked. Continuous monitoring of data validity is essential during the training process. If possible, using distinct training models for various use cases might assist to reduce potential problems or data manipulation.

4 - Model Denial of Service

Model denial of service attacks happen when an LLM is forced to do resource-intensive activities. This might result in reduced service capabilities as well as greater operating and repair expenses.

To assist against denial of service attacks, utilise input validation and content filtration. Setting restrictions on application programming interface (API) requests per user and controlling resource consumption per request are critical preventive measures. Resource monitoring and queue management can also help to limit the likelihood of such attacks. 

5 - Supply Chain Vulnerabilities

Vulnerable components or services inside LLMs might expose users to extra security threats. These flaws might come from using pre-trained models, plugins, or third-party datasets.

Mitigating supply chain risks requires a thorough examination of all providers and suppliers. Effective LLM management requires the use of only trustworthy plugins as well as the implementation of rigorous security mechanisms like as monitoring, anomaly detection, and inventory management.

6 - Sensitive Information Disclosure

LLMs may mistakenly expose secret material in their replies, resulting in privacy violations, security breaches, and illegal access to data.

Organisations should not only control access to information, but also utilise validation to filter harmful inputs, preventing LLM poisoning. Data sanitization techniques may be used to properly sanitise user data for training. If sensitive data is necessary to fine-tune the LLM model, further precautions and processes should be employed.

7 - Insecure Plugin Design

A plugin for an LLM may have unsafe inputs or insufficient access constraints. This makes them much easier for attackers to exploit, possibly leading to major vulnerabilities like remote code execution.

To prevent unsafe plugin design vulnerabilities in LLMs, all plugins must include parameter restrictions, such as validation layers and type checks. It is also critical to implement least-privilege concepts, authorised identities, and user confirmation in procedures. Furthermore, thorough testing should include static application security testing (SAST), dynamic application security testing (DAST), and interactive application security testing (IAST).

8 - Excessive Agency

LLM systems may do activities that result in unanticipated outcomes due to excessive functionality, authorization difficulties, or an abundance of autonomy.

Plugin functionalities and scope should be constrained to minimise excessive agency, as well as open-ended functions. Additionally, controlling rights and enabling user authentication are critical. Downstream systems should always include authorization mechanisms.

9 - Over Reliance

Individuals or systems that rely too much on LLMs risk increasing difficulties such as as miscommunications, disinformation, legal challenges, and security vulnerabilities owing to poor control and testing. This dependency also increases the possibility of creating and disseminating erroneous or harmful material.

Consistent LLM outcomes require ongoing monitoring and validation. Verification from reliable sources is encouraged, as is fine-tuning when possible. To reduce risks, difficult processes should be divided, and all users should be well informed of LLM restrictions. For example, displaying these risks clearly on the user interface. All users should also understand best practices for LLMs and have explicit usage instructions that they strictly follow.

10 - Model Theft

Model theft is the unlawful access, copying, or removal of proprietary LLM models. Such thefts can disclose sensitive or secret information, result in financial losses, and diminish competitive advantage.

Stringent access and authentication procedures are required to avoid model theft. Regular monitoring and auditing of access logs is required to discover any illegal or unintentional access. It is advised to use ML operations (MLOps) automation to ensure secure deployment and approval procedures.

Deployment Challenges in LLM Models

Building a demonstration application based on sample data sets is simple; however, developing and deploying an application that addresses corporate problems is far more difficult.  Here are some of the challenges:

Scaling Prompt Engineering: Customising prompts for LLMs is difficult, especially when dealing with ambiguity, model compatibility, and maintenance, particularly if user participation is involved.

Latency: LLMs such as GPT-4 or GPT-3.5 can be sluggish, with reaction times of 5 to 30 seconds, making them unsuitable for real-time applications.

Optimisation Patterns: Optimising LLMs for individual business challenges requires negotiating a plethora of alternatives and inconsistent methodologies, whether through quick optimisation or model fine-tuning.

Tooling: Existing tools for developing LLM apps are either in early development or nonexistent, necessitating extensive modification.

Agent Policies: Integrating human input into training control agents for specific jobs, a critical component of obtaining Enterprise AGI, is still an issue with no shown answer.

Read more: How Index.dev helped Genemod amplify R&D cloud solutions with high-performing full-stack developers

Rising Demand for LLM Engineers

The need for engineers specialising in large language models (LLMs) has skyrocketed due to the proliferation of these models. Experts in building, optimising, and implementing sophisticated AI models are what these individuals are known for. There has been a recent uptick in the demand for LLM engineers from businesses of all stripes looking to implement AI solutions for data analysis, automated content creation, and customer support chatbots.

Businesses are realising the potential of LLMs to drive innovation and efficiency, which is fueling the need, thanks to the rising use of AI technology. On top of that, you'll need an in-depth knowledge of machine learning in addition to domain-specific apps to manage and optimise these models. The need for LLM engineers is strong in today's tech-driven world since their knowledge is becoming increasingly important as firms try to incorporate AI into their processes more smoothly.

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Conclusion 

Large language models (LLMs) have emerged as revolutionary technologies with enormous promise across several sectors. From OpenAI's GPT to Google's Gemini and beyond, these models provide unparalleled capabilities for understanding and producing human language. Despite their extraordinary value, installing LLMs presents a number of obstacles including prompt engineering, latency issues, and guaranteeing security against vulnerabilities such as  model theft. Organisations may use the potential of LLMs to improve productivity, streamline processes, and drive innovation by carefully considering use cases, conducting extensive research on available choices, and adhering to security best practices specified by efforts such as OWASP. As the subject of LLMs evolves, staying updated on innovations and best practices will be critical to maximise the benefits of these ground-breaking technologies.