Multi-Turn Human–LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol

Our interest is in the design of software systems involving a human-expert interacting–using natural language–with a large language model (LLM) on data analysis tasks. For complex problems, it is possible that harnessing human expertise and creativity to modern LLM capabilities may be able to find solutions that were otherwise elusive. But how is such an interaction to proceed? At one level, the answer is: through multiple turns of prompts from the human and responses from the LLM. Here we investigate a more structured approach based on an abstract protocol described in [3] for interaction between agents. The protocol is motivated by a notion of “two-way intelligibility” and is modelled by a pair of communicating finite-state machines. We provide an implementation of the protocol, and provide empirical evidence of using the implementation to mediate interactions between an LLM and a human-agent in two areas of scientific interest (radiology and drug design). We conduct controlled experiments with a human proxy (a database), and uncontrolled experiments with human subjects. The results provide evidence in support of the protocol’s capability of capturing one- and two-way intelligibility in human LLM interaction; and for the utility of two-way intelligibility in the design of human-machine systems. Our code is available at https://github.com/karannb/interact.