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.

翻译：大型语言模型（LLM）在自然语言处理（NLP）任务中的应用正迅速增长，这导致研究人员处理该领域问题的方式发生变化。为充分利用这些模型的能力，需要更深入地理解它们在不同输入协议下的行为表现。通过LLM，用户可直接通过文本界面与模型交互，定义并解决各类任务。因此，理解这些（可能未专门针对对话建模训练的）LLM的对话能力也至关重要。本研究从提示的多个维度出发，考察了利用LLM构建对话系统的不同方法。在提示调优过程中，我们实验了提供指令、示例、当前查询及额外上下文的各种方式。研究还分析了具有最优有效信息密度的对话历史表示方式。基于研究发现，本文提出了更简洁的对话历史信息提供方法，在确保良好性能的同时降低模型推理API成本。该研究有助于深入理解如何有效利用LLM构建交互式系统。