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Learning to Retrieve In-Context Examples for Large Language Models
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abstract
Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiveness of in-context learning is heavily reliant on the quality of the selected examples. In this paper, we propose a novel framework to iteratively train dense retrievers that can identify high-quality in-context examples for LLMs. Our framework initially trains a reward model based on LLM feedback to evaluate the quality of candidate examples, followed by knowledge distillation to train a bi-encoder based dense retriever. Our experiments on a suite of $30$ tasks demonstrate that our framework significantly enhances in-context learning performance. Furthermore, we show the generalization ability of our framework to unseen tasks during training. An in-depth analysis reveals that our model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes. The code and data are available at https://github.com/microsoft/LMOps/tree/main/llm_retriever .
Forward citations
Cited by 6 Pith papers
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True Multimodal In-Context Learning Needs Attention to the Visual Context
A 160-parameter attention-scaling method, DARA, improves true multimodal in-context learning on a new dataset, TrueMICL, that forces models to use demo images rather than copy text patterns.
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Retrieval Augmented Generation based Large Language Models for Causality Mining
Dynamic retrieval of few-shot examples by causal connective and semantic similarity improves LLM causality detection and extraction over static prompting in most tested settings.
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Learning to Select In-Context Demonstration Preferred by Large Language Model
A generative preference-learning method trains a latent demonstration selector from LLM feedback and improves few-shot in-context learning performance on most of 19 benchmark datasets.
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MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
MAPLE uses graph-influence scores to select and pseudo-label the most useful unlabeled examples, then adaptively chooses demonstrations per query, improving many-shot in-context learning with few human labels.
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Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
In long-context models, more demonstrations do not automatically help; repeating hard examples and appending the model's own zero-shot predictions gives small, inconsistent gains.
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Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning Framework
POCL wraps LLM knowledge distillation in a curriculum that increases data difficulty and temperature over stages, improving Rouge-L on small GPT-2 and OPT students, though ablations show temperature drives the gains.
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