The world of AI has seen enormous progress in the development of Large Language Models (LLMs) — neural networks that can understand, generate, and reason about human language. Models like OpenAI's GPT-4 contain billions (perhaps trillions) of parameters and have achieved human-level performance on various Natural Language Processing (NLP) tasks like text generation, machine translation, question answering, and more.
The rise of powerful LLMs has driven significant excitement and investment in artificial intelligence. Their ability to perform complex language understanding and generation seemed almost magical when first unveiled. However, these models also present real concerns, including bias and lack of transparency, high environmental costs, threats to privacy and security, and limitations in generalizing to new domains.
LLMs work by analyzing huge datasets of text to detect patterns in language. As they get larger and more data-hungry, they require enormous computational resources for training and deployment, consuming huge amounts of energy and raising carbon emissions. They also amplify the biases and flaws contained in their training data, and they struggle to apply knowledge in one area to new domains. Despite their impressive capabilities, we are still far from developing LLMs with the broad, flexible intelligence that humans possess.
Lettria’s Vision for Addressing LLM Challenges
Challenges with LLMs today
Training and running large language models consume a massive amount of energy and contribute significantly to greenhouse gas emissions, which can negatively impact the environment. This issue has gained significant attention in recent years, with some researchers estimating that training a large language model can emit as much CO2 as driving a car for a year. To tackle this problem, Lettria has developed AutoLettria, which enables the training of smaller models that can outperform LLMs on specific tasks such as multi-label classification. These smaller models can run on much smaller servers, reducing the energy consumption and environmental impact.
Language models are trained on massive datasets, which can contain biased language, leading to biased outputs. This can perpetuate and reinforce systemic inequalities. To handle this challenge, Lettria manages training datasets and provides clear accuracy on performance for each label, along with explainability with patterns. By doing so, Lettria aims to provide unbiased outputs and reduce the potential for systemic inequalities.
Data Privacy and Security
Large language models require vast amounts of data, which can raise concerns about data privacy and security. There have been instances where large language models have been used to extract sensitive information from public datasets. To address this issue, Lettria enables the training and deployment of models on private clouds, allowing users to keep full control and compliance with GDPR.
Lack of Common Sense
Although large language models have advanced significantly in natural language processing, they still lack common sense and reasoning abilities that humans possess, leading to occasional nonsensical or inappropriate responses. To confront this obstacle, Lettria uses a knowledge graph to provide structured information, enabling users to access information and apply their own reasoning. By doing so, Lettria aims to provide more accurate and appropriate responses.
Limitations in Handling Rare or Unseen Situations
Language models trained on existing data may struggle to handle rare or unseen situations. This is because they are trained on a fixed dataset and may not have the ability to generalize to new or unusual scenarios. To deal with this concern, Lettria provides innovative solutions such as zero-shot classification and ontology enrichment, enabling users to handle rare or unseen situations more effectively.
Large language models may require significant computational resources to train and run, making them inaccessible to many researchers and developers who lack access to such resources. To solve this dilemma, Lettria provides a no-code platform that enables business experts to access these technologies and integrate them into their tools for text analysis without requiring technical expertise.