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In-context Learning with Retrieved Demonstrations for Language Models: A Survey
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Language models, especially pre-trained large language models, have showcased remarkable abilities as few-shot in-context learners (ICL), adept at adapting to new tasks with just a few demonstrations in the input context. However, the model's ability to perform ICL is sensitive to the choice of the few-shot demonstrations. Instead of using a fixed set of demonstrations, one recent development is to retrieve demonstrations tailored to each input query. The implementation of demonstration retrieval is relatively straightforward, leveraging existing databases and retrieval systems. This not only improves the efficiency and scalability of the learning process but also has been shown to reduce biases inherent in manual example selection. In light of the encouraging results and growing research in ICL with retrieved demonstrations, we conduct an extensive review of studies in this area. In this survey, we discuss and compare different design choices for retrieval models, retrieval training procedures, and inference algorithms.
Forward citations
Cited by 8 Pith papers
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Surprise Calibration uses the model's own surprise at each demonstration's label to dynamically correct class priors in in-context learning, improving accuracy on eight NLP benchmarks.
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CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer
CrossICL reuses demonstrations from similar source tasks, rewriting them into the target task's format, and improves zero-shot ICL on Super-NI without target-task annotations.
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Learning to Select In-Context Demonstration Preferred by Large Language Model
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DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph
DCG-SQL retrieves text-to-SQL demonstrations by embedding a question-to-schema link graph, improving execution accuracy on Spider by up to about 10 points over random demonstrations on small LLMs.
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Diversity-aware selection of in-context examples improves performance on complex and out-of-distribution tasks, though effect sizes are often modest.
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