Papers with adversarial testing
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications (2023.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) are rapidly becoming more and more popular, but dealing with the potential harms associated with their deployment in real-world scenarios is still an open research question. |
| Approach: | They propose an automated approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. |
| Outcome: | AART generates evaluation datasets with high diversity of content characteristics critical for effective adversarial testing. |
A Simple, Yet Effective Approach to Finding Biases in Code Generation (2023.findings-acl)
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| Challenge: | Recent work shows that large language models can generate code on par with humans . however, data-driven approaches may not be sufficient for acquiring reasoning skills . |
| Approach: | They propose a framework that automatically identifies subtle cues a code generation model might exploit . they propose an automated intervention mechanism reminiscent of adversarial testing . |
| Outcome: | The proposed framework can be used as a data transformation technique during fine-tuning, acting as reversal strategy. |