大语言模型时代对话分析的必要性：任务、技术与趋势综述
The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends
AI总结：
本文针对大语言模型时代对话分析（CA）缺乏明确范围导致技术分散的问题，系统定义了CA任务及其四个关键步骤，综述了现有技术、基准与挑战，指出研究正从浅层元素分析向复杂因果与策略任务演进。
摘要：
在大语言模型（LLMs）时代，得益于语言用户界面的快速发展趋势，海量的对话日志将被积累起来。对话分析（CA）致力于从对话数据中揭示和分析关键信息，简化人工流程并支持商业洞察与决策。CA提取可操作洞察并驱动赋能的需求日益凸显，并引起了广泛关注。然而，CA缺乏明确的范围导致各种技术分散，难以形成系统性的技术协同来赋能商业应用。在本文中，我们进行了彻底的回顾并系统化了CA任务，以总结现有的相关工作。具体而言，我们正式定义了CA任务，以应对该领域碎片化和混乱的现状，并从对话场景重建，到深入归因分析，再到执行针对性训练，最后基于针对性训练生成对话以实现特定目标，由此推导出CA的四个关键步骤。此外，我们展示了相关基准，讨论了潜在挑战，并指出了工业界和学术界的未来方向。鉴于当前的进展，显然大部分努力仍集中于浅层对话元素的分析，这在研究与商业之间呈现出相当大的差距，并且在LLMs的辅助下，近期的工作显示出向因果性和策略性任务研究的趋势，这些任务是复杂且高层次的。所分析的经验和洞察在针对对话日志的商业运营中将不可避免地具有更广泛的应用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
