Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language (2504.19856v3)

Abstract: Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a LLM (LM) on its pretraining task, e.g., masked LLMing (MLM), when common domain adaptation via LM fine-tuning is not possible due to a lack of labeled task data. Although popular, MLM requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that the best configuration of ICL-APT performed better than the state-of-the-art DAPT by 28.7% (7.87 points) and requires almost 4 times less GPU-computing time, providing a cost-effective solution for industries with limited computational capacity. The findings highlight the broader applicability of this framework to other low-resource industries, making NLP-based solutions more accessible and feasible in production environments.

Efficient Domain-Adaptive Continual Pretraining for the Process Industry in German

The paper "Efficient Domain-Adaptive Continual Pretraining for the Process Industry in the German Language" by Zhukova et al. presents an innovative approach to domain-adaptive continual pretraining (DAPT) tailored for the German-language process industry. The paper proposes a novel method known as ICL-augmented pretraining or ICL-APT, which leverages in-context learning (ICL) and k-nearest neighbors (kNN) to enhance domain-specific text data augmentation, thereby optimizing the pretraining process for LMs.

Methodological Advances

The central theme of the paper is addressing the inherent challenges in applying DAPT to non-English and low-resource domains like the process industry, characterized by limited availability of large corpora and high computational demands. ICL-APT introduces a methodical augmentation of target datasets, facilitating the acquisition of domain semantics without exhaustive computational resources.

1. k-Nearest Neighbors (kNN) Technique: By employing kNN, the model efficiently retrieves semantically similar documents from domain-related (DR) and in-domain (ID) datasets. This not only enhances the quality of training data but also diminishes the dependency on large-scale corpora typical of conventional DAPT approaches.
2. In-Context Learning (ICL): The augmentation process harnesses ICL to provide contextually rich learning environments by concatenating related domain texts. This gives the LLM a broader domain perspective during training, thus improving its understanding of domain-specific terminology and semantics.
3. Language Masking with Variation: The pretraining phase involves multiple iterations of language masking across the augmented dataset. This repeated exposure in varied masked configurations allows the model to grasp an extensive range of domain-specific lexical items within their contextual frames.

Empirical Evaluation

The research benchmarks ICL-APT against established pretraining strategies like DAPT, TAPT, and combinations thereof. When evaluated on semantic search tasks, ICL-APT demonstrated superior performance, achieving improvements in precision, recall, F1 score, mean average precision (MAP), mean reciprocal rank (MRR), and normalized discounted cumulative gain (nDCG). Specifically, it outperformed traditional setups by 3.5 average IR metrics, indicating substantial advancements in retrieval-task effectiveness. Moreover, ICL-APT required approximately four times less training time on GPU, underscoring its efficiency and practicality for application within computationally constrained environments.

Implications and Future Directions

The outcomes presented in the paper have profound implications for NLP applications in specialized, low-resource domains. By providing a cost-effective and efficient mechanism for continual pretraining, ICL-APT enhances accessibility to NLP solutions in production settings where resources are limited.

Theoretically, the methodology could stimulate further exploration into similar resource-efficient pretraining frameworks across diverse domains and languages. Practically, the successful deployment of ICL-APT could pave the way for widespread adoption in industries wherein domain-specific language use is prevalent, ensuring that collection specifics such as shift logs can be accurately interpreted and utilized for process optimization.

Potential future directions may include exploring further optimizations in data retrieval and augmentation, refining ICL methodologies, integrating advances in text encoding for improved semantic representation, and expanding the scope to incorporate real-time data adaptation capabilities.

In summary, the paper showcases a significant advancement in the field of domain-specific continual pretraining, offering a scalable approach that marries efficacy with efficiency, making AI and NLP applications increasingly viable in niche linguistic territories.