In the face of increasingly diverse reader demographics ensuring that content both resonates and remains relevant poses a subtle yet intricate challenge. Traditionally, content personalisation has focused on tailoring tonality, style, and lexicons to suit reader preferences. Recently there have been frameworks aimed at simplification like 'Explain it to me like I'm five' and targeted explanation like 'Explain to me like I'm a scientist'. However, even with modifications to content style, readers might still experience a disconnection when met with examples in the content that diverge from their knowledge and interests, such as for a fashion enthusiast, technological references spanning disparate eras or geographic regions may not be relevant.

In this work, we approach the temporal, region and topic interest entity correspondence problem in which, given an example and its context, the task is to find an alternative example that exists in a target time, region or topic of interest.  In this manner, our work utilises LLMs to enhance readability by adapting entities and context within a document to align closely with varied reader interests, ensuring reading is more engaging, relatable, and factually consistent for diverse readers.

• First, we plan to update the framework such that it uses LLM for paraphrasing the context of an example and generating queries for search engine. These queries will then be used to find alternate examples on the web. This will use LLMs as an Agent and the outputs will not be bounded by knowledge in LLMs. 

• We plan to use this pipeline and create a benchmark dataset which has cross-domain entities, and benchmark LLMs on their inherent knowledge of these correlated entities.