RE-AdaptIR: Improving Information Retrieval through Reverse Engineered
  Adaptation
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.
66797016335cad277cdd25df
William Fleshman
extracted_confirmed
2024-06-24T13:29:06.481Z
64fa215b52e82dd43249f7e4
/avatars/f73424b8fc72073cb0916e2d2cf9ca56.svg
William Fleshman
user
will-fleshman
66797016335cad277cdd25e0
Benjamin Van Durme
RE-AdaptIR: Improving Information Retrieval through Reverse Engineered
  Adaptation
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.
RE-AdaptIR enhances LLM-based information retrieval systems using unlabeled data, improving both domain-specific and zero-shot performance.
reverse engineered adaptation RE-AdaptIR large language models LLMs text-retrieval information retrieval IR zero-shot fine-tuning