We do not train public LLMs. We use commercially pre-trained Large Language Models as part of our information retrieval and augmentation framework. Currently, this includes the use of a Retrieval Augmented Generation (RAG) architecture among other advanced techniques. While we are using the pre-trained LLMs to support the creation of narrative content, the facts in this content are generated from our trusted academic sources. We test this setup rigorously to ensure academic integrity and alignment with the academic ecosystem. Testing includes validation of responses through academic subject matter experts who evaluate the outputs for accuracy and relevance. Additionally, we conduct extensive user testing that involve real-world research and learning scenarios to further refine accuracy and performance.

We are committed to the highest standards of user privacy and security. We do not share or pass any publisher content, library-owned materials, or user data to large language models (LLMs) for any purpose.

While the LLM is a key tool to provide a fluent narrative, answers to user queries are based on our extensive collection of curated scholarly content. This means that our AI-generated responses draw from trusted academic sources, such as Web of Science, ProQuest One, Ex Libris Central Discovery Index, as well as local library collections, rather than broad (and potentially inaccurate or biased) internet content that common chatbots might use.

We strongly believe that we have a critical responsibility to the academic community to mitigate AI-induced inaccuracies. We continuously test our solutions and the results they produce, including through dedicated beta programs and close collaboration with customers and subject matter experts. Our data science expertise helps ensure system accuracy, fairness, robustness and interpretability. Pairing this with our trustworthy, curated content, we significantly reduce the risk of ‘hallucinations’ and misinformation.

Ensuring clarity and trust in our solutions is one of our top priorities. Our conversational discovery and AI-powered tools present a list of academic resources on which their responses are based, so that you can always explore relevant materials for further context.

Data privacy and trust are top priority when designing our AI tools. We comply with data privacy regulations and adhere to the evolving global AI legislation.
As part of the user query, only the content that users themselves input into the query is transmitted to the LLM. No additional data is shared during this process, ensuring the protection of sensitive information. Furthermore, we are not using any of the LLM API endpoints directly but accessing LLMs through a private setup. This ensures that data entered by users in the query stays protected and cannot be seen or accessed by any other party. This approach to data protection aligns with our practices in academic search, where we apply our knowledge in securely managing user information.For more information on our privacy and data protection program, visit: https://clarivate.com/privacy-center/

The ranking and prioritization of sources by our AI-based discovery tools will vary according to the specific characteristics of the user’s query, the user persona, and the context of each query.

The approach to ranking and prioritization is similar to the way it is traditionally done in our discovery solutions. This understanding enables us to present the most relevant and valuable sources first, ensuring that the information provided matches the user’s needs as closely as possible.