Can you tell us a bit about Exfluency and what you do?
Exfluency is a technology company providing hybrid intelligence solutions for multilingual communication. Leveraging AI and blockchain technology, we give tech-savvy businesses access to modern language tools. Our goal is to make language assets as valuable as other corporate assets.
What technology trends do you see emerging in the field of multilingual communication?
As in every other sector, AI in general and ChatGPT in particular are at the top of the agenda. Companies operating in the language space are either panicking or desperately trying to catch up. The main challenge is the magnitude of the skills gap in the space. Innovation, especially AI innovation, is not plugged in.
What are the benefits of an LLM?
Off-the-shelf LLMs (ChatGPT, Bard, etc.) have the allure of instant results – well-crafted answers magically appear on the screen. It's hard not to be impressed.
The real benefit of LLM comes from players who can provide immutable data to feed into the models – they are the ones we feed into the models.
What does an LLM rely on to learn a language?
Overall, LLMs learn language by analyzing vast amounts of textual data, understanding patterns and relationships, and using statistical techniques to generate context-appropriate responses. Its ability to generalize from data and generate coherent text makes LLMs a versatile tool that can be used for a variety of language-related tasks.
Large-scale language models (LLMs) like GPT-4 rely on a combination of data, pattern recognition, and statistical relationships to learn language. They rely on several key components:
- data: LLM is trained on a huge amount of text data from the internet. This data includes a wide variety of sources, including books, articles, websites, etc. The diversity of the data allows the model to learn a wide range of language patterns, styles, and topics.
- Patterns and RelationshipsLLM learns language by identifying patterns and relationships in data. It analyzes the co-occurrence of words, phrases, and sentences to understand how they fit together grammatically and semantically.
- Statistical LearningLLM uses statistical methods to learn the probabilities of word sequences – estimating the likelihood of a word occurring based on previous words in a sentence – allowing it to generate coherent, contextually relevant text.
- Context informationLLM focuses on contextual understanding – it considers not only the preceding words but also the context of the entire sentence or text. This contextual information helps disambiguate words with multiple meanings and generate more accurate, context-appropriate responses.
- Attention MechanismMany LLMs, including GPT-4, employ attention mechanisms that allow the model to assess the importance of different words in a sentence based on context, which allows the model to focus on relevant information when generating a response.
- Transfer LearningLLM uses a technique called transfer learning, where a model is pre-trained on a large dataset and then fine-tuned for a specific task, allowing the model to adapt to perform specialized tasks such as translation, summarization, and conversation while still leveraging the broad linguistic knowledge from the pre-training.
- Encoder/Decoder ArchitectureFor certain tasks, such as translation or summarization, LLMs use an encoder-decoder architecture: the encoder processes the input text and converts it into a context-rich representation, which the decoder uses to generate output text in the desired language or format.
- Feedback Loop: LLM can learn from user interactions: as users provide corrections and feedback on generated text, the model can adjust its responses over time based on that feedback, improving its performance.
What are the challenges of using an LLM?
The fundamental problem, ever since we started giving our data to Google, Facebook, etc., is that “we” are the product. Big companies make huge profits from our rush to get our data into their apps. ChatGPT, for example, is growing its onboarding faster than ever before. Think about how much Microsoft has made from the millions of prompts people have already thrown at them.
Open LLM is hallucinatory: the answers to the prompts are so well-crafted that you can easily be tricked into believing what the LLM tells you.
To make matters worse, there are no references or links to show where the answers come from.
How can we overcome these challenges?
LLM is what we offer. Blockchain technology allows you to create an immutable audit trail and with it immutable clean data. No need to search the internet. This way you have full control over what data is captured, it remains confidential and is supported with a wealth of useful metadata. It can even be multilingual.
Secondly, this data is stored in our database, so we can also provide you with the source links you need: if you don't believe the prompt answer, just open the source data directly and see who wrote it, when, in what language and in what context.
What advice would you give to companies wanting to use a private, anonymized LLM for multilingual communication?
Make sure your data is immutable, multilingual, high quality, and stored for only you to see, and the LLM is a true game changer.
What do you think the future holds for multilingual communication?
Like many other fields, language will also embrace some form of hybrid intelligence. For example, in the Exfluency ecosystem, AI-driven workflows handle 90% of translation. Our talented bilingual experts only need to focus on the remaining 10%. This balance will shift over time. AI will shoulder an increasing percentage of the workload. But human input will remain crucial. This concept is summed up in our slogan: “Powered by Technology, Completed by Humans.”
What plans does Exfluency have for the next year?
A lot! We aim to deploy this technology into new industries and build and serve our community of small and medium-sized businesses. We've also seen a lot of interest in our Knowledge Mining app, which is designed to leverage the information hidden in millions of linguistic assets. 2024 is going to be an exciting year!
- Jaromir Dzialo is co-founder and CTO of Exfluency, a company that provides affordable AI-powered language and security solutions for organizations of all sizes, powered by a global talent network.
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