The Imperative of Conversation Analysis in the Era of LLMs: A Survey of
  Tasks, Techniques, and Trends
Authors: name: Xinghua Zhang, name: Haiyang Yu, name: Yongbin Li, name: Minzheng Wang, name: Longze Chen, name: Fei Huang

In the era of large language models (LLMs), a vast amount of conversation
logs will be accumulated thanks to the rapid development trend of language UI.
Conversation Analysis (CA) strives to uncover and analyze critical information
from conversation data, streamlining manual processes and supporting business
insights and decision-making. The need for CA to extract actionable insights
and drive empowerment is becoming increasingly prominent and attracting
widespread attention. However, the lack of a clear scope for CA leads to a
dispersion of various techniques, making it difficult to form a systematic
technical synergy to empower business applications. In this paper, we perform a
thorough review and systematize CA task to summarize the existing related work.
Specifically, we formally define CA task to confront the fragmented and chaotic
landscape in this field, and derive four key steps of CA from conversation
scene reconstruction, to in-depth attribution analysis, and then to performing
targeted training, finally generating conversations based on the targeted
training for achieving the specific goals. In addition, we showcase the
relevant benchmarks, discuss potential challenges and point out future
directions in both industry and academia. In view of current advancements, it
is evident that the majority of efforts are still concentrated on the analysis
of shallow conversation elements, which presents a considerable gap between the
research and business, and with the assist of LLMs, recent work has shown a
trend towards research on causality and strategic tasks which are sophisticated
and high-level. The analyzed experiences and insights will inevitably have
broader application value in business operations that target conversation logs.