Large Language Models (LLMs) drive business growth through services such as answering questions, composing emails and generating code. They improve generative AI applications and are known for their human-like text generation capabilities. LLMs are a powerful tool for companies due to their extensive training on large amounts of text from different domains and the recognition of patterns within the text. The parameters that influence the capabilities of LLMs play a central role, but are often misunderstood. Misunderstandings exist regarding the function of the parameters, their different types and the influence of their size on the performance of LLMs. This blog post clarifies these issues.

What is the significance of parameters in Large Language Models?

Parameters are customisable Settings that limit the text generation capabilities of a Large Language Models (LLMs) steer. They influence the variety, creativity and quality of the generated text and serve to optimise the performance of the model. Adjusting parameters improves the process of predicting the next token in a sequence. A token is a unit of text such as a word, word combination or punctuation that is formatted for processing by the LLM.

The training process of an LLM begins by setting the parameters to an initial value based on previous training or random values. The Model is trained with large amounts of text data. It accepts input and predicts the corresponding output. This prediction is then compared with the actual text to check its accuracy. The model learns iteratively from errors and continuously adjusts its parameters to increase the accuracy of the prediction.

Through this iterative process of prediction, error checking and parameter adjustment, the LLM becomes increasingly precise and powerful in its linguistic capabilities.

Thanks to their human-like text generation, large language models improve technological efficiency in companies and are used in a wide range of applications in the business world.

An Introduction to Large Language Models

Types of parameters

Below you will find an overview of the different types of LLM parameters and their advantages. This compilation provides a guide to the effective use of parameters through examples that illustrate the impact of different settings and values on the output. The choice of parameter values should always be tailored to the specific modelling application and business objectives.

• TemperatureThis parameter controls the randomness in the text generation process and influences the quality, diversity and creativity of the results. A high temperature setting produces diverse and unpredictable responses by making the model select less likely tokens. A low temperature setting leads to more coherent and consistent answers by favouring more frequent tokens. For example, for the question "What is the best way to learn programming?", a high temperature value of 1.0 may lead to creative but imprecise answers such as "The best way to learn programming is to go back in time and meet the inventors of programming languages". A low value of 0.1, on the other hand, would provide a predictable and practical answer such as "The best way to learn programming is by practising a lot and following online tutorials". Extreme temperature settings should be avoided as they can lead to nonsensical outputs.
• Number of tokensThis parameter Controls the length of the generated text. A higher number of tokens leads to longer, detailed outputs, while a lower number leads to short and concise answers. The choice of the number of tokens should be based on the purpose and requirements of the application. For the question "What is an LLM?", a higher token count (e.g. 100) could lead to a detailed explanation such as "It is a model that is trained on large amounts of data. It interprets human language and generates responses to prompts. LLMs can generate poems, articles, reports and other texts". A low token count (e.g. 10) would provide a shorter response such as "It is a model that generates human-like text". Extreme values should be avoided as they can lead to redundant or incomplete outputs.
• Top-pThis parameter controls the selection of words during text creationby limiting the candidates for the next word. A high top-p value leads to diverse and creative answers, while a low value provides more accurate and predictable results. For example, for the statement "The most important skill of an accountant is", a high top-p value of 0.9 would lead to creative answers such as "The most important skill of an accountant is telepathy", while a low value of 0.1 would produce a more factual answer such as "The most important skill of an accountant is problem solving". It is important to find a balance to avoid excessive variation in the quality of the results.
• Presence PenaltyThis parameter influences how strongly the generated output reflects the presence of certain words or expressions. A high presence penalty encourages exploration of different topics and avoids repetition, while a low penalty can lead to redundant output. For the statement "The best sport is", a high presence penalty of 1.0 would lead to a diverse response such as "The best sport is football, cricket, chess", while a low penalty of 0.0 could lead to "The best sport is football, football, football". The parameter penalises the model for reusing previously used tokens regardless of their occurrence in the prompt.
• Frequency PenaltyThis parameter scales based on the frequency of a token in the text, including the prompt. Tokens that occur more frequently receive a higher penalty, which reduces their probability. This encourages novelty and variation in the text and reduces repetition. For example, for the statement "The best sport is football", a high frequency penalty leads to a more varied output such as "The best sport is football, football is fun and exciting". A low penalty of 0.0, on the other hand, could lead to a repetitive output such as "The best sport is football, football is fun, football is full of excitement". Here too, a balanced setting of values is crucial to ensure a coherent and meaningful output.

Find out when companies should train their own Large Language Model or fine-tune an existing model to achieve the best added value.

An Introduction to LLM Training

How does the number of parameters influence the performance of an LLM?

Data Scientists often ask the question of the optimum number of parameters for a Large Language Model (LLM). The influence of parameter size on the performance of an LLM is explained below. Finally, the different application areas of large language models and their specific requirements.

A common misconception is that a higher number of parameters automatically leads to better performance. It is true that a model with more parameters can process human speech in greater detail, as it can make more adjustments to capture linguistic complexity. Nevertheless, the The number of parameters alone is not decisive for the performance of a model. Rather, the quality of the Training dataavailable computing resources and the specific requirements of the respective application. A model that has been trained on high-quality data can capture semantic subtleties better than a model of the same size that has been trained with low-quality data. A small model with high-quality training data therefore outperforms a large model based on poor data.

Models with many parameters are generally expensive to implement, require more memory and have longer processing timeswhich limits their efficiency and accessibility. It therefore often makes more sense to utilise the available resources efficiently, for example by fine-tuning the parameters. Targeted optimisation of parameters for specific tasks can be of greater benefit to companies than simply increasing the number of parameters. Different applications require different parameter settings: One Chatbot for example, requires settings that enable natural conversations, while a text generation tool should produce precise and structured articles.

As LLMs evolve, their requirements also change. With the publication of ChatGPT As an open source tool, the initial focus was on the wide range of possible uses and business applications. However, with the increasing spread and the associated impact on users, data protection issues also came to the fore, triggering discussions about the ethical use of LLMs. Companies are now asking their teams to train more efficient models that meet customer requirements and to develop specialised models for specific applications. Initially, companies focussed on training large LLMs with large amounts of data. Recognising the high costs and efficiency benefits of smaller models, the trend is now shifting towards mini LLMs (or Small Language Models, SLMs).

Companies must utilise the opportunities offered by artificial intelligence while ensuring that their applications comply with legal and ethical standards. Find out here how to set up compliant processes:

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Overview of the number of parameters of large language models

The names of LLMs often consist of an acronym followed by a number that indicates the number of parameters the model contains, such as Vicuna-13B or Llama-7B. The number after the hyphen indicates the number of parameters, which indicates the complexity and capacity of the model. The following is a tabular overview of prominent Large Language Models (LLMs) and some Small Language Models (SLMs) and their number of parameters. It should be noted that the exact number of parameters may vary depending on the specific version and configuration of the model, so the values given should be understood as approximate.

Large language models are transforming interaction with technology and expanding its application from content creation to customer service. Our overview presents 14 relevant representatives in detail:

The 14 Top Large Language Models: A Comprehensive Guide

Optimal parameter selection as the key to the efficiency of large language models

Parameters are essential components that enable an LLM to function efficiently. The various parameters include temperature, token count, top-p, presence penalty and frequency penalty, with each parameter influencing the generated result in a specific way. The choice of parameter values should be carefully matched to the specific business application and purpose, avoiding extremely high or low values to prevent undesirable results. For an LLM to perform optimally, a combination of several factors is crucial; simply increasing the number of parameters does not automatically guarantee better performance.