LARGE LANGUAGE MODELS FOR BUSINESS NEEDS
Since the explosive popularity of artificial intelligence (AI) tools, a variety of related terms are increasingly used in everyday life, including the term “large language models” (LLM). What exactly are LLMs and how can they be useful for business?
WHAT ARE LARGE LANGUAGE MODELS?
Large Language Models are high-quality AI models that aim to generate text that is similar to human-generated text, and to adapt this text to a wide variety of needs. LLMs are built and trained with a significant amount of data, so that the computer program can “guess” the required text in different languages as accurately as possible, and can shape and transform the text according to the context.
Using the latest machine learning technologies, the models are able to reproduce natural, “human” language, making it possible to interact with a given dataset, just like talking to another human or robot in a chat box.
MAIN LLM CHARACTERISTICS
1. Understanding language features
Each language has its own cultural characteristics, so many phraseologies and expressions that are specific to one language cannot be translated literally, but rather adapted to a similar phraseology or expression. Large language models can recognise expressions and their meaning not only literally but also figuratively, thus more accurately reading the purpose, emotional tone and other lexical and stylistic categories relevant to a given text.
2. Context with memory
A key difference between LLM and, for example, simple machine learning, is the ability to maintain a specific context within a single conversation – most often a chat – which reduces the need to analyse data, as a programmer would do with precise, mathematical commands. LLM does this as if talking to a well-known colleague. This way, it is possible to recall data or actions discussed, for example, at the beginning of a chat, while preserving the context.
3. Adaptability
LLM can be adapted to different contexts and styles, with the dataset tailored to the specific writing style, level of formality and other characteristics that meet the specific needs of the company. By adjusting the model this way, you don’t have to repeat different queries or commands to get text that is closer to your requirements.
HOW DO LARGE LANGUAGE MODELS WORK?
LLM is powered by neural networks, which have made a significant contribution to text “guessing” and generation. Unlike traditional machine learning methods, a neural network inspired by the human brain reads the meaning of words and sentences to better understand their context. Here are three key terms that better explain LLM’s core business more precisely.
1. Training with data
LLM is trained by feeding the model with a significant amount of textual information to help it more accurately discern linguistic features and patterns. The better the quality of the input data, the better the model will be able to not only recognise similar information, but also generate it.
2. Creation of tokens
Given the size of LLM and the nature of machine learning, words, parts of words or even individual characters are marked with a specific token. This type of categorisation reduces the amount of information to be processed and allows you to predict more accurately which words or symbols will follow each other to form a logical, well-articulated sentence. The more accurately LLM is trained, the better it will be able to generate quality text.
3. Self-learning
LLM’s ability to self-learn, based on data and instructions tailored to the specific industry and company, is also important to improve the quality of the content produced and save time. This is particularly important when developing and using an AI tool at the same enterprise level and adapting it to common needs.
EXAMPLES OF THE USE OF LARGE LANGUAGE MODELS
Large language models can be used to do different kinds of work by adapting them accordingly. Here are some of the most common examples today.
Content creation
Depending on the amount and complexity of the training data, you can create first drafts of texts for blog or social media posts. All you need to do is to specify the desired style, the approximate topics to be covered, and you can get a first draft of the desired content. The better the selection of LLM data, the better the quality of the draft. Once you have a draft of the text, it will be useful to have the perspective of language experts to make it more readable and comprehensible.
Data analysis
Similar to text generation, LLM is useful when analysing a table of specific data sets in PDF or other formats. All that’s left is to set the parameters by which the data should be reviewed and analysed.
Market research
If LLM includes specific data, for example only customer feedback on products purchased from an online shop, then future trend forecasts can be made on the basis of this information.
Customer support
One of the most common uses of LLM is to provide more customer support by embedding conversational interfaces on websites that answer customer questions, analyse them and find the most appropriate answer from the available data model.
Translation
In this global and fast-paced information age, it is essential to inform customers in their own language. Translating any material with the help of professional translators – even with the help of translation tools (CATs) – can be expensive and time-consuming, and while machine translation tools do not always meet a company’s quality criteria, they can still be useful to get a first insight into the specific material. LLM may initially seem unsuitable for translation, but with the right commands or training (coaching), it is gradually possible to produce a high-quality and appropriate text.
ADVANTAGES AND DISADVANTAGES OF LARGE LANGUAGE MODELS
As with most technologies, large language models have both advantages and disadvantages. Let’s take a look at a few.
LLM advantages
- Saving time. Creating a data model with specific information from which to generate text or interpret information can save time previously spent on manual, monotonous tasks.
- Scaling. LLM can process much more information than a single data analyst could sift through hundreds of documents. Similarly, content can be created on a large scale from a single template.
- Customisable. Businesses are more successful with LLM when it can be permanently transformed or personalised so that it can deliver answers or content in the exact style the business needs, without additional queries.
- Cost savings. Using LLM to translate text without additional editing can lead to significant savings – especially if such material is not subsequently used for external company communications. LLM is also able to answer questions better than traditional chatbots by reading the meaning of the customer’s question. There are plenty of opportunities to save.
LLM disadvantages
- Contextual misunderstanding. Although it is mentioned that in most cases LLM tools are able to read the context of the text, in cases where there is not enough data in the model itself to generate a good quality answer, the text may be misunderstood and the final result may not be what you expect.
- Incorrect or inaccurate text / context. In the absence of good quality LLM data, artificial intelligence can get “confused” and produce incorrect text, inventing false facts, citing fake studies, i.e. creating inaccurate information. Giving an AI tool inaccurate instructions can also confuse it. To avoid this, it is recommended to give one instruction per query.
- Time invested. Unless LLM is specifically tailored to the needs of a particular company, which can be quite costly, for an AI tool to provide exactly the information needed, time needs to be invested in polishing the exact query or “prompt”.
- Changes to the LLM model. If the model, its sub-models and their training are not controlled by the company itself, then over time, even with a well-designed query, the data obtained may become less predictable and of lower quality. For example, an AI may refuse to provide data analysis on the grounds that such a task is beyond its technical capabilities.
HOW CAN SKRIVANEK HELP?
With expertise. As mentioned above, there are many different aspects to consider when choosing a traditional approach for text production and/or translation, or taking advantage of the possibilities offered by large language models.
Each has its own advantages and disadvantages – from cost to the need for additional expertise to make the text readable, comprehensible and understandable for the end consumer. For example, what works for converting or localising technical, predictable material may not be effective enough for marketing or creative content.
If you already have marketing materials created with LLM, but want to fine-tune them to better match your company’s identity, we offer an AI-generated marketing text checking and proofreading service.
We also help with automated machine translation solutions for businesses by adding neural network processing to the translation process – just like the LLM model mentioned above. Here, translators join in the post-production process to improve the material.
However, if you want the undivided attention of a human, we also offer traditional localisation with translation tools (CAT), where frequently used terms and expressions are already automatically suggested from a stored memory that is embedded with your company’s style.
Contact us, and together we will find the best solution for your company!