DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning
arxiv(2024)
摘要
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.
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