kNN-BOX: A Unified Framework for Nearest Neighbor Generation

Abstract: Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick development and interactive analysis for this novel paradigm. kNN-BOX decomposes the datastore-augmentation approach into three modules: datastore, retriever and combiner, thus putting diverse kNN generation methods into a unified way. Currently, kNN-BOX has provided implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. It is easy for users to reproduce these existing works or customize their own models. Besides, users can interact with their kNN generation systems with kNN-BOX to better understand the underlying inference process in a visualized way. In the experiment section, we apply kNN-BOX for machine translation and three other seq2seq generation tasks, namely, text simplification, paraphrase generation and question generation. Experiment results show that augmenting the base neural model with kNN-BOX leads to a large performance improvement in all these tasks. The code and document of kNN-BOX is available at https://github.com/NJUNLP/knn-box.

• The paper introduces a unified framework that decomposes nearest neighbor generation into modular components for easier implementation and reproducibility.
• It demonstrates broad applicability by achieving significant performance improvements in machine translation, text simplification, paraphrase, and question generation.
• The framework offers interactive visualization tools to enhance interpretability and supports efficient adaptation across multi-domain and multilingual tasks.

Overview of "$k$NN-BOX: A Unified Framework for Nearest Neighbor Generation"

The paper "$k$NN-BOX: A Unified Framework for Nearest Neighbor Generation" introduces an innovative framework designed to enhance the capabilities of base neural models by integrating a symbolic datastore approach into generation tasks like machine translation (MT). This framework is significant because it unifies diverse $k$NN (k-nearest neighbor) generation methods, facilitating not only implementation but also the augmentation, reproduction, and visualization of $k$NN-based models.

Key Contributions

• Unified Framework: $k$NN-BOX is conceived to standardize the integration of symbolic datastores with neural models by decomposing the process into three distinct modules: datastore, retriever, and combiner. This modular approach allows researchers to easily apply, modify, or combine $k$NN methods in a cohesive manner.
• Broad Applicability: The paper demonstrates $k$NN-BOX's versatility by applying it to not only machine translation but also three other sequence-to-sequence tasks: text simplification, paraphrase generation, and question generation. Experiments indicate significant performance improvements across these tasks when using $k$NN-BOX.
• Interpretability and Visualization: The framework includes tools for visual analysis, allowing users to interactively explore the inference process of their models. This feature aims to enhance understanding and transparency of $k$NN-augmented neural systems, addressing an ongoing need within the research community for interpretable AI models.
• Implementation of Diverse Methods: $k$NN-BOX includes implementations of seven prominent $k$NN-MT models, enabling users to rapidly reproduce prior work or create new models by leveraging the provided base components.

Experimental Insights

The paper provides experimental results indicating robust improvements in BLEU scores across a range of datasets and tasks. For example, $k$NN-BOX achieves high translation quality with varying datastore scales, highlighting the framework's efficiency in both performance enhancement and datastore optimization. Notably, multi-domain adaptation and multilingual MT further showcase the adaptability and efficacy of $k$NN augmentation.

Implications and Future Directions

The $k$NN-BOX framework holds the potential for broad theoretical and practical implications. By facilitating unified implementations of $k$NN-model methods, it paves the way for streamlined model comparisons and novel integrations in AI research. Additionally, by providing an open-source platform, $k$NN-BOX encourages ongoing collective development and innovation in the community.

Future research directions may include expanding the framework to support long-range sequence generation tasks and further optimizing the interactive features for an improved user experience. Furthermore, continued exploration of the interpretability of $k$NN-BOX could yield deeper insights into how memory-augmented systems can influence neural network behaviors.