Summary of Detoxbench: Benchmarking Large Language Models For Multitask Fraud & Abuse Detection, by Joymallya Chakraborty et al.
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection
by Joymallya Chakraborty, Wei Xia, Anirban Majumder, Dan Ma, Walid Chaabene, Naveed Janvekar
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical application in high-stake domains, such as fraud and abuse detection, remains an area that requires further exploration. This paper presents a comprehensive benchmark suite designed to assess the performance of LLMs in identifying and mitigating fraudulent and abusive language across various real-world scenarios. The benchmark encompasses a diverse set of tasks, including detecting spam emails, hate speech, misogynistic language, and more. We evaluated several state-of-the-art LLMs from Anthropic, Mistral AI, and AI21 family to provide a comprehensive assessment of their capabilities in this critical domain. The results indicate that while LLMs exhibit proficient baseline performance in individual fraud and abuse detection tasks, their performance varies considerably across tasks, particularly struggling with tasks that demand nuanced pragmatic reasoning, such as identifying diverse forms of misogynistic language. These findings have important implications for the responsible development and deployment of LLMs in high-risk applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how well big language models can help catch bad language online. Right now, these models are good at detecting certain kinds of mean or hurtful speech. But they’re not perfect, and sometimes they struggle to understand more complex problems. The researchers created a special test to see how well the models do at catching different types of bad language. They looked at many top-performing models from different places like Anthropic, Mistral AI, and AI21 family. What they found out is that while the models are good at detecting some kinds of mean speech, they’re not very good at understanding more subtle forms of bullying or hate speech. This means we need to keep working on making these models better and more reliable for catching bad language online. |
Keywords
* Artificial intelligence * Natural language processing
