Reduce Language Friction, Produce Content Fast
AI-powered content generation has become ubiquitous. From chatbots to automated news articles, machine learning algorithms are being leveraged to create written content at an unprecedented scale. However, a significant challenge arises when the source material is in a language other than English, the primary language most large language models (LLMs) are trained on.
The Translation Pain Point
Generating AI-powered content from non-English sources involves a complex, multi-step process. First and foremost, the text needs to be translated into English. But translation is far from straightforward, as languages have their own unique nuances, colloquialisms, and context-specific expressions that can be difficult to accurately convey in another tongue.
Even the most advanced translation models can only get you halfway there - extensive human review and editing are still necessary to ensure the English output is coherent and faithful to the original meaning.
The Source-Medium Challenge
Furthermore, a lot of valuable information, especially from less-resourced languages, may not be available in a digitized format. It could be locked away in old books, handwritten notes, or even photographs and scans that first need to be transcribed and converted to machine-readable text.
All of these pre-processing steps add significant complexity and effort before the content can be "embedded" or used as a prompt in an LLM. Embedding is a mathematical process used for sorting and idetifiying similarities in meaning and context within content.
What we are doing about this
This is the challenge that Lexiqn is aiming to tackle. Lexiqn provides a seamless end-to-end workflow that abstracts away the complicated process of ingesting non-English content and preparing it for use in generative AI.
Whether it's recognizing scanned images, perceiving and transcribing handwritten text, translating to English, or contextualizing and cataloging the information, Lexiqn handles it all behind the scenes.
All the user needs to do is probe for answers from the cataloged content and generate new content as needed.
Our own experience
Lexiqn is only a result of our own experience with this problem. As amature collectors of stories and folklore from different cultures around the world, we realized that many of the most fascinating tales were locked away in less-resourced languages without good digital records.
Driven by a passion to share some of these stories with a wider audience in some of our blogging platforms, we built Lexiqn to enable us to produce content often, leveraging seamless research and analysis of the source content. In other words, to be prolific in producing content.
The possibilities to strive for
We know many efforts and projects are under way to leverage LLMs for less-resourced languages. We are simply an extension of that community.
Maybe Lexiqn will help writers and researchers access information that was previously inaccessible due to language barriers. Imagine being able to read through decades' worth of newspaper archives from Indonesia, folk tales from indigenous communities in the Amazon, or historical epics from Sub-Saharan Africa - all at the click of a button and in fluent English.
The possibilities for drawing insights and writing compelling, original content are endless.
The hope and the challenge
In the short term, our aim is for Lexiqn to enable users with prolific content creation by drastically reducing the time and effort required to research and draw inspiration from non-English sources.
Who knows? The more content we generate based on the less-resourced languages, the more translated data AI models will have to train themselves on other languages.
As we continue to grapple with the challenges and opportunities presented by generative AI, we believe small innovations will be crucial in ensuring that the benefits of AI are felt by all of humanity, not just the English-speaking world.