Adversarial Response

Adversarial response research focuses on improving the robustness of machine learning models, particularly large language models (LLMs), against misleading or malicious inputs. Current research investigates how LLMs handle adversarial examples in various tasks, including question answering, dialogue evaluation, and response selection, often employing techniques like retrieval-augmented generation (RAG) and prompt engineering to mitigate the negative impact of such inputs. This work is crucial for building more reliable and trustworthy AI systems, addressing vulnerabilities to manipulation and improving the safety and effectiveness of human-computer interaction. The development of robust algorithms and evaluation benchmarks for handling adversarial responses is a key focus, with implications for numerous applications relying on LLMs.