Improving Domain Adaptation through Extended-Text Reading Comprehension (2401.07284v2)

Abstract: To enhance the domain-specific capabilities of LLMs, continued pre-training on a domain-specific corpus is a prevalent method. Recent work demonstrates that adapting models using reading comprehension data formatted by regex-based patterns can significantly improve performance on domain-specific tasks. However, regex-based patterns are incapable of parsing raw corpora using domain-specific knowledge. Furthermore, the question and answer pairs are extracted directly from the corpus in predefined formats offers limited context. To address this limitation, we improve reading comprehension via LLM and clustering. LLM focuses on leveraging domain knowledge within the corpus to refine comprehension stage, while clustering supplies relevant knowledge by extending the context to enrich reading stage. Additionally, our method incorporates parameter-efficient fine-tuning to improve the efficiency of domain adaptation. In comparison to AdaptLLM, our method achieves an improvement exceeding 5% in domain-specific tasks. Our code will available at https://github.com/microsoft/LMOps.

• The paper introduces a novel LLM-based preprocessing method that generates high-quality question-answer pairs from complex domain-specific corpora.
• It employs length-based clustering and parameter-efficient fine-tuning (LoRA) to enhance context comprehension and model adaptation.
• Empirical results show over a 5% performance improvement in specialized domains such as biomedicine and finance compared to regex-based methods.

Improving Domain Adaptation through Extended-Text Reading Comprehension

This paper contributes an advancement in domain adaptation techniques for LLMs by utilizing extended-text reading comprehension methodologies. The researchers propose a novel approach leveraging LLMs and clustering techniques to address specific limitations identified in regex-based patterns used by existing models like AdaptLLM.

Overview and Methodology

The paper aims to enhance the domain-specific capabilities of LLMs by refining the reading comprehension paradigm. Traditional approaches, such as AdaptLLM, rely heavily on regex-based patterns to transform corpora into structured question-answer formats. However, these patterns struggle with complex domain-specific knowledge extraction and provide limited context.

To overcome these challenges, the researchers developed a multifaceted approach:

1. LLM-based Data Preprocessing: By employing models like ChatGPT, the approach generates high-quality question-answer pairs from the domain corpus. This addresses the inadequacies of regex patterns by better capturing domain nuances. Additionally, they fine-tune a smaller LLM to efficiently preprocess extensive datasets, mitigating the cost implications of using API-based LLMs.
2. Length-based Clustering: The method enhances context comprehension by clustering similar documents, thus extending the input context. This is particularly beneficial in domains like biomedicine, where documents such as abstracts tend to be concise but require deeper contextual understanding.
3. Parameter Efficient Fine-Tuning: The paper explores the use of LoRA for parameter-efficient fine-tuning, showing that with appropriate settings, it can outperform traditional full fine-tuning methods, especially for domain-specific knowledge embedding.

Experimental Results

Empirical evaluations demonstrate substantial performance improvements over existing models. The method achieves over 5% enhancement in domain-specific tasks compared to AdaptLLM. Noteworthy results were obtained for biomedicine and finance domains:

• Biomedicine Domain: The approach yielded an average improvement in performance metrics across several datasets, including PubMedQA and BioMMLU.
• Finance Domain: Similar performance gains were observed, with significant improvements on datasets like ConvFinQA and FPB.

These results emphasize the efficacy of integrating extended-text context through clustering and the strategic use of LLM-based preprocessing.

Implications and Future Directions

The methodological advancements presented in this paper have significant theoretical and practical implications:

• Improved Domain Adaptation: The integration of LLMs for data preprocessing and extended context through clustering could redefine domain adaptation strategies, making them more robust and contextually aware.
• Efficiency in Model Adaptation: By demonstrating the effectiveness of parameter-efficient tuning, the research provides a road map for more resource-effective adaptation processes, potentially broadening the accessibility of advanced AI models to various applications.

Future research may explore further the scalability of these techniques across other domains, such as legal or industrial applications. Additionally, refining clustering algorithms to dynamically adjust context lengths or integrating more sophisticated LLMs could offer even greater performance enhancements.

In conclusion, this paper makes a significant contribution by proposing an innovative approach to domain adaptation, leveraging enhanced reading comprehension techniques to improve model performance in domain-specific tasks. The findings open new avenues for efficient model training and adaptation in complex domains, offering both practical benefits and theoretical insights.