Tech Deep Dive: Domain Adaptation – Enhancing Iris.ai AI Model for Specialized Knowledge
Welcome back to our Tech Deep Dive series! In the ever-expanding world of scientific research, having access to accurate and relevant information is crucial. In this blog post, we’ll delve into the concept of domain adaptation, its pros and cons, and explore how we implement it in our tools. So let’s dive deep into the world of domain adaptation and its significance in enhancing the performance of AI models.
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What is Domain Adaptation?
Domain adaptation is essential when a general AI model lacks the accuracy required for a specific domain. Domain adaptation is the process of injecting domain-specific knowledge into an existing fundamental AI model trained on a general broad set of documents (in our case documents from all sciences). By doing so, the model becomes more accurate and the encoded knowledge in it better represents the specialized domain.
The primary reason for domain adaptation is to address the limitations of a general AI model. While the current model performs well across various scientific domains, it may lack the necessary accuracy and domain representation i.e. lack of knowledge for that domain, underrepresented knowledge or wrong interpretations of certain concepts. By adapting the model to a particular domain, Iris.ai ensures that clients can find more precise and detailed knowledge within that specific area of interest.
Pros and Cons of Domain Adaptation
The advantages of domain adaptation lie in the improved representation of specialized knowledge within the AI model. By injecting new domain-specific knowledge, the model becomes more accurate, leading to better results for clients in their specific domain of interest. However, the necessity for additional training data, client involvement and model evaluations can be considered as a downside. If this process is not properly automated and not carefully designed to ease client’s involvement it can’t be scaled. Nevertheless, the benefits of domain adaptation outweigh the challenges it presents.
The Process of Domain Adaptation
So how exactly do we perform domain adaptation at Iris.ai? Imagine a mathematical space that represents general scientific knowledge. Within this space, domain-specific knowledge forms a subspace. Our objective is to enhance the representation within this subspace while maintaining the existing representation in the surrounding space. To achieve this, we inject more data specific to the desired domain, thereby strengthening the model’s representation of that domain.
Obtaining and Filtering Domain-Specific Data
The process of domain adaptation involves injecting domain-specific data into the existing embedding model. Iris.ai starts with a general model trained on a large set of scientific articles. When a client requires domain adaptation, they provide an initial set of domain-specific papers. For instance, when working with Materiom, they initially provide us with a set of 10-15 papers. Using our Explore tool, we expand on this initial set by finding articles similar to those provided by the client. Once we reach approximately 5,000 articles, we use this domain-specific dataset to fine-tune the existing model. This process involves training the model using the domain-specific data, ensuring its adaptation to the desired domain.
Considerations and Costs
The minimum number of documents a client needs to provide for domain adaptation varies, but even a small dataset can work. While the process of domain adaptation itself is relatively fast, the cost associated with it arises from additional infrastructure requirements. To accommodate domain-adapted models, Iris.ai creates separate environments for clients. A specific machine is set up to host the domain-specific data and the adapted embeddings model. This ensures that the client can access the precise knowledge within their domain without interference from the general model.
Comparing Techniques and Future Research
At Iris.ai, we have explored different techniques for domain adaptation. The technique currently in use involves fine-tuning the existing model with domain-specific data. This approach allows the model to retain both the general scientific knowledge and the injected domain-specific knowledge. We have published papers discussing related techniques, such as injecting a knowledge graph into an embedding space and exploring domain adaptation in spherical embeddings. These research endeavors enable us to compare different techniques and determine the most effective approach for our domain adaptation process.
Key Takeaways
Domain adaptation is an essential process that enables us to enhance the accuracy and relevance of AI models for specific domains. By injecting domain-specific knowledge into our existing models, we improve the representation of the desired domain, enrich the vocabulary used in that domain, and boost the priority of domain-specific concepts. While challenges such as need for client involvement and additional costs exist, the benefits of domain adaptation far outweigh these factors. At Iris.ai, we are committed to continually refining our domain adaptation techniques and exploring new possibilities to provide the most accurate and relevant knowledge to our clients.