title: The Imperative of Conversation Analysis in the Era of LLMs: A Survey of
    Tasks, Techniques, and Trends
summary: 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.
paper_authors._id: 66f6a945cd978208b9e30aee
paper_authors.name: Xinghua Zhang
paper_authors._id: 66f6a945cd978208b9e30aef
paper_authors.name: Haiyang Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-30T08:07:00.648Z
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paper_authors.avatarUrl: /avatars/859a189c5d2ae2fcb9aa2d79104fbfe7.svg
paper_authors.fullname: Haiyang Yu
paper_authors.type: user
paper_authors.user: yhycai
paper_authors._id: 66f6a945cd978208b9e30af0
paper_authors.name: Yongbin Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-14T10:47:49.935Z
paper_authors._id: 66641b2fd8e1e34bc621e688
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
paper_authors.fullname: Yongbin Li
paper_authors.type: user
paper_authors.user: Yongbin-Li
paper_authors._id: 66f6a945cd978208b9e30af1
paper_authors.name: Minzheng Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-08T08:17:34.920Z
paper_authors._id: 64bcc373ef8c0e42bf16acc5
paper_authors.avatarUrl: /avatars/873308203d28115ae1a9e4d0e26508f4.svg
paper_authors.fullname: mz.w
paper_authors.type: user
paper_authors.user: iiiiwis
paper_authors._id: 66f6a945cd978208b9e30af2
paper_authors.name: Longze Chen
paper_authors._id: 66f6a945cd978208b9e30af3
paper_authors.name: Fei Huang
paper_title: The Imperative of Conversation Analysis in the Era of LLMs: A Survey of
    Tasks, Techniques, and Trends
paper_summary: 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.
paper_ai_summary: Conversation Analysis task is systematized into four key steps, leveraging large language models to bridge the gap between research and business by focusing on causality and strategic tasks.
paper_ai_keywords: large language models Conversation Analysis conversation scene reconstruction in-depth attribution analysis targeted training causality strategic tasks