Disentangling Questions from Query Generation for Task-Adaptive Retrieval
<|reference_start|>Disentangling Questions from Query Generation for Task-Adaptive Retrieval: This paper studies the problem of information retrieval, to adapt to unseen tasks. Existing work generates synthetic queries from domain-specific documents to jointly train the retriever. However, the conventional query generator assumes the query as a question, thus failing to accommodate general search intents. A more lenient approach incorporates task-adaptive elements, such as few-shot learning with an 137B LLM. In this paper, we challenge a trend equating query and question, and instead conceptualize query generation task as a "compilation" of high-level intent into task-adaptive query. Specifically, we propose EGG, a query generator that better adapts to wide search intents expressed in the BeIR benchmark. Our method outperforms baselines and existing models on four tasks with underexplored intents, while utilizing a query generator 47 times smaller than the previous state-of-the-art. Our findings reveal that instructing the LM with explicit search intent is a key aspect of modeling an effective query generator.<|reference_end|>
@article{lee2024disentangling,
    title={Disentangling Questions from Query Generation for Task-Adaptive
  Retrieval},
    author={Yoonsang Lee, Minsoo Kim, Seung-won Hwang},
    journal={arXiv preprint arXiv:2409.16570},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16570},
    primaryClass={cs.CL}
}