3 accepted papers
Large language models usually face continuous domain shifts during testing, which degrade performance on unseen shifting domains. So, researchers propose continual test-time adaptation (CTTA) to adapt to evolving testing domains while preserving knowledge of previous domains, making adaptability-sta…
LLMs with in-context learning (ICL) obtain remarkable performance but are sensitive to the quality of ICL examples. Prior works on ICL example selection explored unsupervised heuristic methods and supervised LLM-based methods, but they typically focus on the selection of individual examples and igno
Watermarking enables people to determine whether the text is generated by a specific model. It injects a unique signature based on the “green-red” list that can be tracked during detection, where the words in green lists are encouraged to be generated. Recent researchers propose to fix the green/red…