Unlocking and Understanding the Retrieval Potential of Frozen Generative Models

Generative models encode rich information about their inputs, yet their default hidden-state representations are often poorly suited for retrieval. We investigate the retrieval potential of frozen generative models and show that optimizing a few continuous guiding embeddings, with under 50K trainable parameters, yields effective retrieval representations without modifying backbone weights or causal attention. Experiments on three retrieval datasets demonstrate substantial improvements over frozen baselines, with performance approaching or exceeding specialized retrievers. Even a single guiding embedding, requiring only a few thousand parameters in total, retains competitive performance. The learned guiding embeddings also generalize well across datasets without further training. Beyond retrieval quality, our representation analysis reveals that distinct guiding embeddings learned in separate runs steer the same frozen backbone toward increasingly similar input-dependent representations. Queries and candidate answers encoded using guiding embeddings from different runs can be matched with little average performance loss, demonstrating functional compatibility beyond comparable retrieval scores. Together, these findings reveal substantial retrieval capability accessible through remarkably small adaptations to frozen generative models. This capability is not tied to a unique set of guiding embeddings. Distinct learned input parameterizations can induce closely aligned representations and compatible retrieval behavior within the same frozen backbone.