RE-AdaptIR: Improving Information Retrieval through Reverse Engineered
  Adaptation
Authors: name: William Fleshman, name: Benjamin Van Durme

Large language models (LLMs) fine-tuned for text-retrieval have demonstrated
state-of-the-art results across several information retrieval (IR) benchmarks.
However, supervised training for improving these models requires numerous
labeled examples, which are generally unavailable or expensive to acquire. In
this work, we explore the effectiveness of extending reverse engineered
adaptation to the context of information retrieval (RE-AdaptIR). We use
RE-AdaptIR to improve LLM-based IR models using only unlabeled data. We
demonstrate improved performance both in training domains as well as zero-shot
in domains where the models have seen no queries. We analyze performance
changes in various fine-tuning scenarios and offer findings of immediate use to
practitioners.