提出新查询生成方法EGG，让检索模型更灵活适应多样搜索意图。
Disentangling Questions from Query Generation for Task-Adaptive Retrieval
- 将查询生成视为意图编译，而非简单问答转换
- 在BeIR四类任务上超越现有模型，且模型小47倍
- 适合需要轻量高效检索适配的场景
本文研究信息检索中对未见任务的自适应问题。现有方法通过领域文档生成合成查询以联合训练检索器，但传统查询生成器将查询视为问题，难以覆盖通用搜索意图。更灵活的方法采用任务自适应技术，如使用1370亿参数大模型进行少样本学习。本文挑战将查询等同于问题的主流趋势，提出将查询生成视为高层意图到任务自适应查询的“编译”过程。为此，我们提出EGG查询生成器，在BeIR基准上更好适配广泛搜索意图。该方法在四项具有未充分探索意图的任务上优于基线和现有模型，同时使用的查询生成器仅为之前最先进模型的1/47。研究发现，显式指示语言模型的搜索意图是构建有效查询生成器的关键。
原文摘要 · Abstract (English)
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.
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