Several significant benchmarks have been developed to evaluate language understanding and specific applications of large language models (LLMs). Notable benchmarks include GLUE, SuperGLUE, ANLI, LAMA, TruthfulQA, and Persuasion for Good, which assess LLMs on tasks such as sentiment analysis, commonsense reasoning, and factual accuracy. However, limited work has specifically targeted fraud and abuse detection using LLMs, with challenges stemming from restricted data availability and the prevalence of numeric datasets unsuitable for LLM training.
The scarcity of public datasets and the difficulty in textual representation of fraud patterns have underscored the need for a specialized evaluation framework. These limitations have driven the development of more targeted research and resources to enhance the detection and mitigation of malicious language using LLMs. A new AI research from Amazon introduces a novel approach to address these gaps and advance LLM capabilities in fraud and abuse detection.
Researchers present “DetoxBench,†a comprehensive evaluation of LLMs for fraud and abuse detection, addressing their potential and challenges. The paper emphasises LLMs’ capabilities in natural language processing but highlights the need for further exploration in high-stakes applications like fraud detection. The paper underscores the societal harm caused by fraud, the current reliance on traditional models, and the lack of holistic benchmarks for LLMs in this domain. The benchmark suite aims to evaluate LLMs’ effectiveness, promote ethical AI development, and mitigate real-world harm.
DetoxBench’s methodology involves developing a benchmark suite tailored to assess LLMs in detecting and mitigating fraudulent and abusive language. The suite includes tasks like spam detection, hate speech, and misogynistic language identification, reflecting real-world challenges. Several state-of-the-art LLMs, including those from Anthropic, Mistral AI, and AI21, were selected for evaluation, ensuring a comprehensive assessment of different models’ capabilities in fraud and abuse detection.
The experimentation emphasizes task diversity to evaluate LLMs’ generalization across various fraud and abuse detection scenarios. Performance metrics are analyzed to identify model strengths and weaknesses, particularly in tasks requiring nuanced understanding. Comparative analysis reveals variability in LLM performance, indicating the need for further refinement for high-stakes applications. The findings highlight the importance of ongoing development and responsible deployment of LLMs in critical areas like fraud detection.
The DetoxBench evaluation of eight large language models (LLMs) across various fraud and abuse detection tasks revealed significant differences in performance. The Mistral Large model achieved the highest F1 scores in five out of eight tasks, demonstrating its effectiveness. Anthropic Claude models exhibited high precision, exceeding 90% in some tasks, but had notably low recall, dropping below 10% for toxic chat and hate speech detection. Cohere models displayed high recall, with 98% for fraud email detection, but lower precision, at 64%, leading to a higher false positive rate. Inference times varied, with AI21 models being the fastest at 1.5 seconds per instance, while Mistral Large and Anthropic Claude models took approximately 10 seconds per instance.
Few-shot prompting offered a limited improvement over zero-shot prompting, with specific gains in tasks like fake job detection and misogyny detection. The imbalanced datasets, which had fewer abusive cases, were addressed by random undersampling, creating balanced test sets for better evaluation. Format compliance issues excluded models like Cohere’s Command R from final results. These findings highlight the importance of task-specific model selection and suggest that fine-tuning LLMs could further enhance their performance in fraud and abuse detection.
In conclusion, DetoxBench establishes the first systematic benchmark for evaluating LLMs in fraud and abuse detection, revealing key insights into model performance. Larger models like the 200 Billion Anthropic and 176 Billion Mistral AI families excelled, particularly in contextual understanding. The study found that few-shot prompting often did not outperform zero-shot prompting, suggesting variability in prompting effectiveness. Future research aims to fine-tune LLMs and explore advanced techniques, emphasizing the importance of careful model selection and strategy to enhance detection capabilities in this critical area.
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