  The use of large language models (LLMs) in natural language processing (NLP)
tasks is rapidly increasing, leading to changes in how researchers approach
problems in the field. To fully utilize these models' abilities, a better
understanding of their behavior for different input protocols is required. With
LLMs, users can directly interact with the models through a text-based
interface to define and solve various tasks. Hence, understanding the
conversational abilities of these LLMs, which may not have been specifically
trained for dialog modeling, is also important. This study examines different
approaches for building dialog systems using LLMs by considering various
aspects of the prompt. As part of prompt tuning, we experiment with various
ways of providing instructions, exemplars, current query and additional
context. The research also analyzes the representations of dialog history that
have the optimal usable-information density. Based on the findings, the paper
suggests more compact ways of providing dialog history information while
ensuring good performance and reducing model's inference-API costs. The
research contributes to a better understanding of how LLMs can be effectively
used for building interactive systems.
