Recent developments in large language models (LLMs) have shown promise in
their ability to generate synthetic query-document pairs by prompting with as
few as 8 demonstrations. This has enabled building better IR models, especially
for tasks with no training data readily available. Typically, such synthetic
query generation (QGen) approaches condition on an input context (e.g. a text
document) and generate a query relevant to that context, or condition the QGen
model additionally on the relevance label (e.g. relevant vs irrelevant) to
generate queries across relevance buckets. However, we find that such QGen
approaches are sub-optimal as they require the model to reason about the
desired label and the input from a handful of examples. In this work, we
propose to reduce this burden of LLMs by generating queries simultaneously for
different labels. We hypothesize that instead of asking the model to generate,
say, an irrelevant query given an input context, asking the model to generate
an irrelevant query relative to a relevant query is a much simpler task setup
for the model to reason about. Extensive experimentation across seven IR
datasets shows that synthetic queries generated in such a fashion translates to
a better downstream performance, suggesting that the generated queries are
indeed of higher quality.

最近大型语言模型（LLMs）的发展表明，通过仅给出少量示例，可以生成合成的查询 - 文档对，从而为建立更好的信息检索模型提供支持，特别是对于没有可用训练数据的任务。本文提出通过同时为不同标签生成查询来减轻 LLMs 的负担，通过大量实验发现，以这种方式生成的合成查询在下游任务中表现更好，说明生成的查询确实具有更高的质量。