A ct2 P : LLM -Driven Online Dialogue Act Classification for Power Analysis (2025)

Abstract: In team communication, dialogue acts play a crucial role in helping team members understand each other’s intentions and revealing the roles and communication patterns within interactions. Although existing studies have focused on using Dialogue Act classification to capture the speaker’s intentions, few have explored the underlying power dynamics reflected by these dialogue acts. To this end, we present an online Dialogue Act Classification and Dynamic Power Analysis framework—Act2P, which is based on large language model. The framework combines the zero-shot learning capability of LLMs and introduces an online feedback classification method that allows for online classification with iterative feedback to previous stages, achieving efficient and accurate classification without the labeled data. Additionally, we also propose the PowerRank algorithm, which quantifies power dynamics through a graph-based structure. Through comparative experiments with existing methods, we demonstrate the significant superiority of Act2P in online scenarios and successfully visualize dialogue power in online, clearly presenting the distribution and dynamic transfer of power. This framework provides new scientific insights and practical tools for optimizing team collaboration.

## introduction effective communication is crucial in team-based tasks, influencing collaboration efficiency and task outcomes. analyzing interaction patterns can reveal underlying relationships, optimizing teamwork. dialogue act classification @xcite plays a key role in natural language processing by identifying user intent. however, existing researchs primarily focus on explicit utterance functions @xcite @xcite , overlooking the implicit power dynamics embedded in dialogue. different dialogue acts often involve power exertion, acceptance, or resistance, significantly affecting team collaboration and decision-making.power has been extensively studied in sociology, management, and linguistics, with traditional research emphasizing stable hierarchical structures. however, power in team interactions is inherently dynamic, continuously evolving throughout a conversation. members' speech patterns, responses, and engagement influence power distribution. traditional dac methods rely heavily on manually labeled data, limiting their adaptability across domains. inconsistencies in annotation schemes further reduce transferability and generalizability, leading to performance degradation in new contexts. these challenges hinder the integration of dialogue act classification with power quantification, restricting the ability to analyze online power shifts in team interactions. large language models @xcite @xcite offer a breakthrough in addressing these challenges. their zero-shot learning capabilities enable dialogue act classification without requiring extensive annotations, allowing for greater adaptability across different datasets. prompt engineering @xcite enhances llms' ability to classify dialogue acts efficiently, providing online analytical support. additionally, llm facilitate power shift detection, advancing the study of dynamic power quantification in conversation. to address the aforementioned limitations, this paper proposes the act2p framework, an online dialogue act classification and dynamic power analysis method based on large language model(llm). act2p leverages the powerful language understanding capabilities of llm to achieve zero-shot classification of dialogue acts. one of its key innovations is the introduction of an online feedback classification method, which iteratively optimizes the model based on online feedback, enabling rapid adaptation to datasets with limited or no human annotation and effectively improving classification accuracy and dynamic adaptability. additionally, the act2p framework introduces a power dynamic quantification algorithm based on dialogue acts, which can capture and reflect the flow and changes of power among team members online, providing new methods and perspectives for power analysis in team communication. therefore, the main contributions of this paper are as follows: in summary, act2p not only overcomes the limitations of traditional dialogue act classification methods in terms of annotation dependency and online applicability but also explores the power dynamics reflected within dialogue acts.. this framework provides a novel theoretical and practical tool for investigating dynamic interactions within teams. research has gradually focused on the impact of dialogue space modeling on classification. @xcite proposed a speaker-turn-aware method that combines speaker information with utterance representations. @xcite used graph structures to integrate contextual information at the speaker level. @xcite and @xcite used graph structures to learn the representations of utterance nodes, improving utterance representation. some studies have used multimodal information for recognition, such as the online multimodal dialogue act classification framework proposed by @xcite , which combines transcribed text and multimodal features for training. this study proposes using large language models (llms) for zero-shot dialogue act classification. compared to existing methods, llms enable efficient classification in the absence of labeled data. power analysis: power dynamics have long been an important research topic in fields such as organizational behavior, psychology, and computational linguistics. hofstede's power distance index (pdi) @xcite provides a theoretical foundation for understanding power distribution in organizations and cultures, measuring the degree of power inequality and its acceptance. in language interactions, researchers focus on how speakers use language to manifest and maintain power. @xcite introduced the linguistic coordination model, which shows that low-power individuals tend to imitate the language style of high-power individuals. @xcite proposed the syntactic alignment model, which demonstrates that low-power individuals imitate not only vocabulary but also syntactic structures. @xcite analyzed the language patterns of leaders and followers, revealing how role settings dynamically influence power. in the email domain, @xcite introduced the power networks framework, which combines neural network prediction models with contextual modeling to accurately predict power relations in email communications. @xcite used supervised learning to classify power based on semantic and structural features, while @xcite analyzed power propagation paths by constructing email communication networks. this study focuses on power dynamics reflected in dialogue acts and employs llms for their quantification and visualization. ## framework act2p is an framework based on large language models, designed for online dialogue act classification and dynamic power analysis.the framework classifies dialogue acts while integrating power quantification algorithms and dynamic visualization techniques to capture and analyze power distribution and shifts in team communication online, offering an efficient tool for collaboration optimization, as illustrated in figure 1 . ## results and analysis we evaluated the performance of the proposed llm-based zero-shot classification framework in online domain adaptation classification tasks (online da classification) and compared it with current related research.in our experiments, we selected the average of 10 trial results for evaluation. ## conclusion we propose an online dialogue act classification and dynamic power analysis framework, act2p, based on large language models (llm), aimed at effectively capturing and quantifying power dynamics in real-time team communication. we demonstrate that the framework, through the design of efficient prompts and online feedback classification, can quickly adapt to different conversational scenarios and perform accurate classification in a zero-shot learning setting. by incorporating power quantification mechanisms, we can monitor and analyze power shifts in real-time, providing in-depth insights into team communication patterns. future work could explore ways to improve dialogue act classification accuracy under different granularities of labels, further enhance the precision of power quantification, and attempt to integrate other collaborative features such as speech information to strengthen the framework's real-time capability and adaptability.