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This paper introduces FRACTURED-SORRY-Bench, a framework for evaluating the safety of Large Language Models (LLMs) against multi-turn conversational attacks.
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022). Red Teaming Language Models with Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 3419-3448)
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Lin, Y. T., & Chen, Y. N. (2023). LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models. In Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023) (pp. 47-58). Association for Computational Linguistics
2023
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2024
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Hassine, J. (2024). An LLM-based Approach to Recover Traceability Links between Security Requirements and Goal Models. In Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (pp. 643-651). Association for Computing Machinery
2024
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