Search engines have difficulty searching into knowledge repositories because they are not tailored to the users’ differing information needs. User queries are, more often than not, under-specified or contain ambiguous terms that also retrieve irrelevant documents. Personalized query reformulation aims to refine queries per user, enhancing the relevance of search results while avoiding semantic drift. This task remains challenging due to the inadequate number of queries in user’s search sessions, let alone a query itself often suffers from ambiguity and is too brief. Existing methods have employed session history or click-throughs to enrich the query context, though one crucial cue has been overlooked: the user herself. In this paper, we propose leveraging conditional transformers such as the text-to-text transfer transformer (t5) to incorporate a user-tailored pretext to the input sequence as prior conditions to generate personalized reformulation of queries in the output sequence. Our experiments on the aol query log demonstrated the effectiveness of t5 in personalized query reformulation, without any loss of generality to other conditional transformers. The codebase to support the reproducibility of our research is available at https://github.com/fani-lab/RePair/tree/uid-wise24 .