While in-context Learning (ICL) has proven to be an effective technique to
improve the performance of Large Language Models (LLMs) in a variety of complex
tasks, notably in translating natural language questions into Structured Query
Language (NL2SQL), the question of how to select the most beneficial
demonstration examples remains an open research problem. While prior works
often adapted off-the-shelf encoders to retrieve examples dynamically, an
inherent discrepancy exists in the representational capacities between the
external retrievers and the LLMs. Further, optimizing the selection of examples
is a non-trivial task, since there are no straightforward methods to assess the
relative benefits of examples without performing pairwise inference. To address
these shortcomings, we propose DeTriever, a novel demonstration retrieval
framework that learns a weighted combination of LLM hidden states, where rich
semantic information is encoded. To train the model, we propose a proxy score
that estimates the relative benefits of examples based on the similarities
between output queries. Experiments on two popular NL2SQL benchmarks
demonstrate that our method significantly outperforms the state-of-the-art
baselines on one-shot NL2SQL tasks.

提出了一种新的演示检索框架 DeTriever，使用大语言模型的隐藏状态的加权组合来学习富语义信息，并通过估计输出查询之间的相似性来评估示例之间的相对好处。在两个热门 NL2SQL 基准测试上的实验表明，该方法在一次性 NL2SQL 任务中显著优于最先进的基准线。