Dynamic Retrieval-Augmented Generation

TL;DR

The paper introduces DRAG, a novel retrieval-augmented generation method that injects compressed entity embeddings to enhance context length and accuracy, especially in code generation, outperforming existing baselines.

Contribution

It proposes a new dynamic retrieval-augmented generation approach that overcomes context window limitations by embedding retrieved entities, with a new dataset for code generation tasks.

Findings

Achieves longer context handling and larger retrieval sets.

Outperforms all baselines except GPT-3.5.

Reduces issues with entity misspelling and retrieval failures.

Abstract

Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hallucinate and lack locally relevant factual data. Retrieval-augmented approaches were introduced to overcome these problems and provide more accurate responses. Typically, the retrieved information is simply appended to the main request, restricting the context window size of the model. We propose a novel approach for the Dynamic Retrieval-Augmented Generation (DRAG), based on the entity-augmented generation, which injects compressed embeddings of the retrieved entities into the generative model. The proposed pipeline was developed for code-generation tasks, yet can be transferred to some domains of natural language processing. To train the model, we collect and publish a new project-level code generation dataset. We use it for the evaluation along with publicly available datasets. Our approach achieves several targets: (1) lifting the length limitations of the context window, saving on the prompt size; (2) allowing huge expansion of the number of retrieval entities available for the context; (3) alleviating the problem of misspelling or failing to find relevant entity names. This allows the model to beat all baselines (except GPT-3.5) with a strong margin.