NLP-ADBench: NLP Anomaly Detection Benchmark (2412.04784v1)

Abstract: Anomaly detection (AD) is a critical machine learning task with diverse applications in web systems, including fraud detection, content moderation, and user behavior analysis. Despite its significance, AD in NLP remains underexplored, limiting advancements in detecting anomalies in text data such as harmful content, phishing attempts, or spam reviews. In this paper, we introduce NLP-ADBench, the most comprehensive benchmark for NLP anomaly detection (NLP-AD), comprising eight curated datasets and evaluations of nineteen state-of-the-art algorithms. These include three end-to-end methods and sixteen two-step algorithms that apply traditional anomaly detection techniques to language embeddings generated by bert-base-uncased and OpenAI's text-embedding-3-large models. Our results reveal critical insights and future directions for NLP-AD. Notably, no single model excels across all datasets, highlighting the need for automated model selection. Moreover, two-step methods leveraging transformer-based embeddings consistently outperform specialized end-to-end approaches, with OpenAI embeddings demonstrating superior performance over BERT embeddings. By releasing NLP-ADBench at https://github.com/USC-FORTIS/NLP-ADBench, we provide a standardized framework for evaluating NLP-AD methods, fostering the development of innovative approaches. This work fills a crucial gap in the field and establishes a foundation for advancing NLP anomaly detection, particularly in the context of improving the safety and reliability of web-based systems.

• The paper introduces NLP-ADBench, the first dedicated benchmark for anomaly detection in NLP, featuring eight diverse datasets and evaluating nineteen state-of-the-art algorithms.
• Experiments show that hybrid methods combining transformer embeddings (especially OpenAI's text-embedding-3-large) with traditional AD algorithms generally outperform selected end-to-end approaches.
• Key findings indicate no single model is universally superior, emphasizing the need for dataset-specific model selection and future research on automated methods and embedding efficiency.

Overview of NLP-ADBench: A Focus on Anomaly Detection in NLP

The paper "NLP-ADBench: NLP Anomaly Detection Benchmark" introduces NLP-ADBench, a benchmark explicitly designed to address the deficit in dedicated benchmarks for anomaly detection in the domain of NLP. Given the burgeoning prevalence of textual data in applications ranging from social media moderation to phishing detection, the importance of detecting content that deviates significantly from expected patterns is paramount. Despite advancements in anomaly detection (AD) for structured data, applications for unstructured textual data remain underdeveloped.

Core Contributions

The research offers several notable contributions to the field of NLP-based anomaly detection:

1. Diverse Spectrum of Datasets: NLP-ADBench incorporates eight datasets derived from various NLP domains. Each dataset is curated to embody typical scenarios encountered in web systems, such as spam detection and content moderation.
2. Comprehensive Evaluation of Algorithms: Nineteen state-of-the-art anomaly detection algorithms are evaluated, including three end-to-end methods (e.g., CVDD, DATE, FATE) and sixteen hybrid approaches that leverage pre-trained language embeddings generated by models such as BERT-base-uncased and OpenAI's text-embedding-3-large.
3. Insightful Findings: The paper determines that no single model outperforms others consistently across all datasets, underscoring the importance of dataset-specific model selection. Notably, two-step methods using transformer-based embeddings provided superior performance compared to the selected end-to-end strategies, with OpenAI embeddings showing significant advantages over those from BERT.
4. Open-Source Framework: By disseminating these datasets and algorithm implementations openly, the research fosters ease of reproducibility and encourages further advancements within the community.

Key Results and Implications

The experiments conducted as part of NLP-ADBench reveal several critical insights:

• Performance Variability: The absence of a universally superior model for all datasets highlights the importance of automated model selection mechanisms. This variability suggests that future research should move toward creating adaptive systems capable of discerning optimal algorithms based on dataset characteristics such as the number and diversity of categories present.
• Superiority of Transformer-Based Embeddings: Analyses showcase that methods combining transformer-generated embeddings with traditional anomaly detection algorithms, such as the OpenAI + LUNAR approach, outperform others in most cases. This indicates the potential in hybrid techniques, especially when addressing varied and complex datasets.
• Cost-Benefit of High-Dimensional Embeddings: The utilization of high-dimensional embeddings from models like OpenAI’s text-embedding-3-large contributes significantly to detection accuracy. However, this comes with the challenge of balancing computational efficiency and performance gains, suggesting that future endeavors should contemplate dimensionally optimized embeddings.

Future Directions

The paper points toward several future research directions:

• Automated Model Selection: Emphasizing the need for systems that can automatically select suitable algorithms based on specific dataset traits, leveraging meta-learning approaches seen in other anomaly detection settings.
• Embedding Efficiency: Future work should consider developing lightweight algorithms that can efficiently utilize the advantages of transformer-based embeddings without incurring significant computational costs. Additionally, ensuring the robustness of these strategies across diverse datasets is vital.
• Dimensionality Optimization: A promising area of exploration involves reducing embeddings’ dimensionality while maintaining robust anomaly detection performance, possibly through adaptive algorithms that adjust to dataset-specific needs dynamically.

In conclusion, the introduction of the NLP-ADBench establishes a valuable foundation for advancing NLP anomaly detection research. By providing a comprehensive benchmarking suite, this work aids in bridging the gap between structured data anomaly detection advancements and the inherently complex nature of text data. Enhanced by open-sourced resources, NLP-ADBench sets the stage for ongoing and future investigations into improving the safety and reliability of web systems through sophisticated anomaly detection mechanisms.