Unsupervised Domain Clusters in Pretrained Language Models (2004.02105v2)

Abstract: The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality. In addition, domain labels are many times unavailable, making it challenging to build domain-specific systems. We show that massive pre-trained LLMs implicitly learn sentence representations that cluster by domains without supervision -- suggesting a simple data-driven definition of domains in textual data. We harness this property and propose domain data selection methods based on such models, which require only a small set of in-domain monolingual data. We evaluate our data selection methods for neural machine translation across five diverse domains, where they outperform an established approach as measured by both BLEU and by precision and recall of sentence selection with respect to an oracle.

• The paper demonstrates that large pre-trained language models inherently form unsupervised domain clusters with high purity using Gaussian Mixture Models.
• It details two data selection methods—Domain-Cosine and Domain-Finetune—that significantly boost neural machine translation performance.
• Empirical evaluations via BLEU scores validate the approach, offering practical improvements for domain adaptation in NLP.

Unsupervised Domain Clusters in Pretrained LLMs: A Detailed Exploration

The paper "Unsupervised Domain Clusters in Pretrained LLMs" investigates the latent representational capabilities of modern, vast pre-trained LLMs, such as BERT and RoBERTa, particularly focusing on their unexpected proficiency in clustering text by domains without any explicit supervision. The authors propose a new, data-driven conceptualization of domains based on unsupervised learning from textual data. Utilizing these capabilities, the paper introduces innovative domain data selection methods beneficial for neural machine translation (NMT), thereby presenting a significant advancement in adapting machine learning models to various linguistic domains through data selection.

Key Contributions and Findings

The research uncovers that large pre-trained LLMs inherently learn sentence representations that can cluster by domains. By using Gaussian Mixture Models (GMMs) to evaluate this clustering property, the authors illustrate that the unsupervised domain clusters formed by pre-trained models have a high degree of purity. Sentence representations were obtained via average pooling over hidden states, showcasing robust performance even without task-specific tuning. Particularly notable was the performance of BERT-based and RoBERTa models which excelled compared to word2vec and autoregressive models like GPT-2 in aligning textual data into coherent domain-based clusters.

Data Selection for Neural Machine Translation

The implications of these findings are profound for domain adaptation in machine translation. By leveraging domain clusters, the authors develop two methods for domain data selection using pre-trained LLMs:

1. Domain-Cosine Method: Selects data based on the cosine similarity of sentence embeddings to a domain-specific query vector.
2. Domain-Finetune Method: Fine-tunes the LLM for binary classification to differentiate in-domain data, enhancing the selection process by leveraging pre-trained knowledge in a task-specific way.

In empirical evaluations using a multi-domain German-English parallel corpus, these techniques demonstrated superior performance, measured through BLEU scores, against traditional methods such as the Moore-Lewis method. The Domain-Finetune method, which utilizes fine-tuning on in-domain data for classification, provided particularly strong results in aligning selections with the quality of oracle-selected data.

Implications and Future Directions

This work paves the way for more efficiently finding relevant data for adaption tasks in natural language processing, a crucial challenge as the field increasingly leverages models trained on massive general-domain corpora. The ability to identify and utilize domain-specific data with minimal supervision could substantially improve machine translation quality across distinct domains, particularly when dealing with data from heterogeneous sources like web-crawled texts.

Looking forward, there are potential explorations into utilizing such domain clustering abilities for multilingual adaptations, interacting with multilingual LLMs, and further refining domain adaptation techniques across a broader spectrum of NLP tasks. Additionally, understanding the intricate domain-learning aspects of these models can aid in optimizing their architectures for specific contexts or reducing computational inefficiencies.

Overall, this paper provides a detailed examination of unsupervised domain clustering capabilities within pretrained LLMs, and harnesses these insights to practical ends, potentially influencing both theoretical understandings and application-oriented advances in natural language processing.