ChaI-TeA: A Benchmark for Evaluating Autocompletion of Interactions with LLM-based Chatbots
This work addresses the time-consuming task of phrasing messages for users of LLM-based chatbots, though it is incremental as it primarily establishes a benchmark for an emerging problem.
The authors tackled the problem of autocompleting user messages in LLM-based chatbot interactions by introducing ChaI-TeA, a benchmark framework with datasets and metrics, and found that current models perform fairly but have room for improvement, particularly in suggestion ranking.
The rise of LLMs has deflected a growing portion of human-computer interactions towards LLM-based chatbots. The remarkable abilities of these models allow users to interact using long, diverse natural language text covering a wide range of topics and styles. Phrasing these messages is a time and effort consuming task, calling for an autocomplete solution to assist users. We introduce the task of chatbot interaction autocomplete. We present ChaI-TeA: CHat InTEraction Autocomplete; An autcomplete evaluation framework for LLM-based chatbot interactions. The framework includes a formal definition of the task, coupled with suitable datasets and metrics. We use the framework to evaluate After formally defining the task along with suitable datasets and metrics, we test 9 models on the defined auto completion task, finding that while current off-the-shelf models perform fairly, there is still much room for improvement, mainly in ranking of the generated suggestions. We provide insights for practitioners working on this task and open new research directions for researchers in the field. We release our framework to serve as a foundation for future research.