Personalized Autocompletion of Interactions with LLM-basedChatbots

Composing messages in chatbot interactions is often time-consuming, making autocompletion an appealing way to reduce user effort. Different users have different preferences and therefore different expectations from autocompletion solutions. We study how personalization can improve the autocompletion process, evaluating four schemes defined along two axes: generation vs. ranking, and prior messages vs. external features. Experiments on the WildChat and PRISM datasets with the Mistral-7B and Phi-3.5-mini models show consistent gains. Our results highlight personalization as a key factor in building effective chatbot autocomplete systems, and assist researchers and practitioners in deciding where and how to invest in improving these solutions.