在LLM時代進行對話分析的必要性：任務、技術和趨勢的調查

The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends

September 21, 2024

作者: Xinghua Zhang, Haiyang Yu, Yongbin Li, Minzheng Wang, Longze Chen, Fei Huang

cs.AI

摘要

在大型語言模型（LLMs）時代，由於語言使用者界面的快速發展趨勢，將積累大量對話記錄。對話分析（CA）致力於從對話數據中揭示和分析關鍵信息，簡化手動流程，支持業務洞察和決策。CA需要提取可操作的見解並推動賦能的需求日益突出，吸引廣泛關注。然而，由於CA缺乏明確的範疇，導致各種技術的分散應用，難以形成系統化的技術協同以賦能業務應用。本文對CA任務進行了全面回顧和系統化，以總結現有相關工作。具體而言，我們正式定義CA任務，以應對該領域的碎片化和混亂局面，並從對話場景重建、深入歸因分析，到執行有針對性的訓練，最終基於有針對性的訓練生成對話以實現特定目標，推導出CA的四個關鍵步驟。此外，我們展示相關基準，討論潛在挑戰，指出行業和學術界的未來方向。從目前的進展來看，顯而易見，大多數努力仍集中在分析表面對話元素，這在研究和商業之間存在著相當大的差距，而借助LLMs，最近的工作顯示出一種趨勢，即對因果關係和複雜高級任務進行研究。分析的經驗和見解將不可避免地在針對對話記錄的業務運營中具有更廣泛的應用價值。

English

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.