Should AI models be like Swiss Army knives, versatile and handy in a variety of scenarios? Or do we prefer them as precision tools, finely tuned for specific tasks? In the world of artificial intelligence, and natural language processing specifically, this is an ongoing debate. The question boils down to whether models trained for specific tasks are more effective at these tasks than general models. Task-specific models: specialization and customization In my last blog post , we looked at the rise of personalized LLMs, customized for specific users. Personalized LLMs can be seen as an extreme form of task-specific model. Fans of task-specific models stress that these kinds of models are better suited for tasks involving confidential or proprietary data. This is obviously true. But some people also believe that specialized models necessarily perform better in their specific domains. It may sound logical, but the ans...
Introduction
The rise of Large Language Models (LLMs) such as ChatGPT has been revolutionary and is poised to radically change society as we know it.
Over the last few months, many companies have started looked into creating their own “personalized LLMs”, tailored with insights derived from their company's specific documentation and data and fine-tuned for specific tasks.
It is anticipated that these so-called Leveraged Pre-trained Language Models (LPLMs) will revolutionize various domains like healthcare, finance, and customer service by enabling more intuitive and personalized interactions, enhanced data analysis, and streamlined decision-making processes.
While the rest of the early 2020s are poised for a significant integration of LPLMs, we can, in the near future, also look forward to Individualized Language Models (ILMs), tailored to suit individual preferences, needs, and purposes.
In an interview with ABC News earlier this year, Mira Murati, Chief Technology Officer at OpenAI (the company behind ChatGPT), emphasized the importance of customization in AI models. She explained that enhancing the models' capabilities to align with user values and beliefs will allow users greater flexibility in tailoring the AI's behavior to their preferences.
The interviewer asked if this customization would lead to a future where individuals have their own personalized AI based on their interests and needs. Murati clarified that while there will have to be certain broad bounds, the aim is customization within those bounds.
During the same interview, Sam Altman, CEO of OpenAI, said about the future of LLMs, “This will be the greatest technology humanity has yet developed. We can all have an incredible educator in our pocket that’s customized for us, that helps us learn, that helps us do what we want.”
What are LPLMs and why do companies want them?
Creating LPLMs involves tailoring the behavior and functionality of language models to suit specific groups of users or contexts. Recent developments in AI mean these models can include multiple modalities, i.e. text, images, audio, and more.
LPLMs can be optimized to match the language and terminology used in specific domains such as legal, medical, technical, or creative writing. This specialization ensures that the model generates responses using the specialized jargon and knowledge relevant to that domain.
Several companies are already using customized in-house or customer-focused LPLMs trained on their personal company data. Crucially, this provides more privacy and customization than would be possible by using a general LLM such as ChatGPT.
The customization means the model can generate more relevant responses and provide information related to the specific company or industry.
For instance, in the legal domain, an LPLM can use legal jargon and provide relevant information about case laws and statutes. It can help draft legal documents by understanding case specifics and providing appropriate legal terminology. Legal professionals can use these models to sift through legal documents, extract relevant information, and provide summaries or insights for case preparations.
However, there is a snag when using language models for information retrieval. LLMs tend to fabricate information (“hallucinate”). LPLMs are no exception.
The hallucination problem
The hallucination problem in language models refers to the tendency of these models to fabricate incorrect information.
Language models are trained on vast amounts of text from various sources. While this helps them learn grammar, syntax, and general language patterns, they do not have access to ground truth and may not have a comprehensive understanding of context. They only know to generate the statistically most relevant next word in a sequence.
As a result, LLMs may sometimes generate responses that sound plausible but are incorrect or fictional.
In the context of specialized domains, hallucinations can be particularly problematic. For example, in the legal domain, a language model may generate responses that seem legally accurate, referencing laws, cases, or legal principles that do not exist. These inaccuracies can have severe consequences, leading to incorrect legal advice.
Luckily, there are ways to mitigate this.
Providing the language model with high-quality, domain-specific training data can help it learn the correct terminology and information for that particular domain. Aligning the model's generated responses with the specialized knowledge of the domain can help reduce the likelihood of hallucinations.
Involving domain experts in the fine-tuning process can further refine the model's understanding by providing expert guidance and oversight.
Counting down to individualized language models
In the near future, advancements in technology will make it possible to create ILMs for individual users. These ILMs will be fine-tuned by taking into account an individual's historical interactions, communication style, vocabulary, and other personal factors. By aligning with an individual's unique linguistic style, preferences, and contextual needs, we can foresee ILMs generating highly personalized and relevant responses.
This personalized approach will be a step toward a future where AI technology will become seamlessly integrated into individualized workflows and able to assist in day-to-day activities.
Coming back to our law example, we can foresee how a lawyer’s personalized language model will be able to understand both their unique communication style and legal terminology.
As suggested by Altman, we can also see personalized language models transforming education by providing tailored assistance. For instance, an ILM may be able to adapt to a student's learning style, generate study guides, offer explanations in preferred formats, and deliver subject-specific content. In language learning, an ILM may provide language exercises, pronunciation assistance, and cultural insights based on the user's target language and learning progress.
We can foresee ILMs acting as a personalized tutor, offering adaptive learning materials, providing instant feedback on assignments, and customizing study plans based on a student's learning pace and weaknesses.
Ethical considerations and privacy concerns
The development of Language Models personalized to individuals, while promising in its potential benefits, also brings ethical considerations. Foremost is the issue of data privacy. The customization of LLMs will mean gathering and analyzing vast amounts of personal data. Safeguarding this data will be imperative to prevent breaches or misuse, including the unauthorized sharing or sale of it.
Future trends and predictions
Predicting the evolution of LLMs in the next few years is challenging due to the rapidly evolving nature of machine learning. However, we can speculate on potential directions based on current trends.
Models are likely to continue growing in size and complexity, enhancing their ability to understand context and generate more nuanced and human-like responses.
Future language models are set to become increasingly specialized for specific domains, industries, or professions, enabling them to understand and generate content tailored to different domains.
Leveraging conversational depth and interactivity, language models might provide more personalized responses by delving into context, emotions, and user intent.
Impacts on society
LPLMs, and especially ILMs, as we have seen, are set to have profound positive impacts on society.
However, alongside these benefits, there are concerns that misinformation and deepfakes will proliferate and pose threats to various aspects of society.
Privacy and ethical considerations will also become paramount. LLMs require significant amounts of data for training and especially in the context of ILMs, this raises questions about data privacy and consent. Striking a balance between leveraging the power of LLMs and safeguarding individual privacy will be a critical societal challenge.
Finally, the transformation brought about by LLMs will extend to job roles and employment dynamics. Automation of various tasks through LLMs could lead to job displacement. This might necessitate a shift towards roles that complement AI technologies and align with new societal needs.
In essence, LLMs will drive collaboration between humans and machines. They will augment productivity, creativity, and problem-solving capabilities across industries and mark a paradigm shift in how we work and interact with technology. Yet, managing the ethical, privacy, and societal implications of this transformation will require careful consideration and proactive measures.
Collaborative efforts between researchers, developers, and policymakers can lead to frameworks that ensure the responsible development and deployment of future LLMs. Continuous ethical considerations should guide their evolution to maximize their positive impact.
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