Efficient Nearest Neighbor Language Models

Abstract: Non-parametric neural LLMs (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints. While effective, these models often require retrieval from a large datastore at test time, significantly increasing the inference overhead and thus limiting the deployment of non-parametric NLMs in practical applications. In this paper, we take the recently proposed $k$-nearest neighbors LLM (Khandelwal et al., 2020) as an example, exploring methods to improve its efficiency along various dimensions. Experiments on the standard WikiText-103 benchmark and domain-adaptation datasets show that our methods are able to achieve up to a 6x speed-up in inference speed while retaining comparable performance. The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-parametric NLMs.

• The paper introduces adaptive retrieval, datastore pruning, and dimension reduction to significantly cut down inference overhead in kNN-based language models.
• Methodologies deliver up to a 6.6x speed-up on WikiText-103 and a 5.4x acceleration on Law-MT, maintaining competitive perplexity scores.
• Empirical results demonstrate practical enhancements for real-time NLP applications, paving the way for efficient, deployable language understanding systems.

Efficient Nearest Neighbor LLMs: A Comprehensive Overview

The paper "Efficient Nearest Neighbor LLMs" by Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick focuses on improving the efficiency of non-parametric neural LLMs (NLMs) that adopt an external datastore, exemplified by the $k$-nearest neighbors LLM ($k$NN-LM). The primary challenge with these models is the significant inference overhead arising from datastore retrieval at test time, which hinders practical application deployment. Through this work, the authors propose multiple methodologies to mitigate this bottleneck, achieving substantial speed-up without sacrificing model performance.

Summary of Key Contributions

The authors present several strategies to enhance $k$NN-LM efficiency: adaptive retrieval, datastore pruning, and dimension reduction. Each method provides a unique approach to balance the trade-off between model performance and inference speed.

1. Adaptive Retrieval: By training a retrieval adaptor, this method dynamically determines when to consult the external datastore based on context. Through a lightweight MLP network relying on features derived from the pretrained NLM and count-based statistics, the retrieval frequency is reduced by 50%, resulting in nearly a two-fold increase in evaluation speed while maintaining performance.
2. Datastore Pruning: Recognizing the redundancy in datastore entries, the authors explore several pruning techniques. While simple random pruning sets a baseline for retaining performance, greedy merging emerges as the most effective. By locally merging entries sharing similar characteristics, it maintains a balance between compression and accuracy.
3. Dimension Reduction: High-dimensional context vectors contribute to inference sluggishness. The paper employs principal component analysis (PCA) to reduce vector dimensionality, leading to notable speed enhancements. Remarkably, a 4x reduction in dimensionality yields minor performance gains on the WikiText-103 dataset, suggesting an alignment of reduced dimensions with more effective vector-space representation for defining $p_{k\text{NN}$.

Empirical Analysis and Results

The introduced techniques are tested on standard language modeling datasets, WikiText-103, and a domain adaptation scenario on Law-MT. The results show that the combination of all proposed methods yields up to a 6.6x speed-up on WikiText-103 while preserving the perplexity close to $k$NN-LM. In the domain adaptation setting (Law-MT), a 5.4x acceleration is achieved with superior perplexity.

• WikiText-103: Combination of the methods without performance loss achieved a 16.67 perplexity at 1835 tokens/sec.
• Law-MT: Improved perplexity to 12.29 while speeding up evaluation to 5708 tokens/sec, confirming the effectiveness of these approaches in domain-specific settings.

Implications and Future Work

This paper's findings highlight the significant impact of integrating adaptive policies and data efficiency principles into non-parametric NLMs. The practical implications are extensive, potentially enhancing real-time applications reliant on LLMs, such as conversational AI and translation systems.

Future exploration could explore the synergy between more advanced indexing methods for retrieval tasks and the strategies presented here. Furthermore, expanding this framework could offer insights into constructing adaptable models capable of leveraging sparse data effectively, which is crucial for broad-scale NLP applications.

In conclusion, this study provides a detailed examination of methods to streamline non-parametric neural LLMs, setting the stage for more efficient and practically deployable language understanding systems.