Year: 2025
Type: conference-paper
Abstract: 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 in both training domains and in zero-shot domains where the models have seen no queries. We analyze performance changes in various fine-tuning scenarios and offer findings of immediate use to IR practitioners.
Authors: William Fleshman, Benjamin Van Durme
Institution: Johns Hopkins University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412378289