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This paper empirically investigates the relationship between salience and answerability of questions in naturalistic dialogue, finding a robust but low positive correlation that is weaker than in monologic text, indicating that conversational structure is less predictable.
Introduces Empath, a framework for analyzing emotion dynamics in crisis counseling dialogues, applied to grief discussions with Black texters, revealing patterns like persistent negative affect and gradual hope transitions.
This study investigates how morality, politeness, and alignment function in human-AI dialogue compared to human-human conversation, finding that AI reproduces surface features of cooperation without underlying social adaptation.
This paper introduces a productivity-oriented framework for evaluating human-AI collaboration based on outcome quality relative to interaction cost, showing that identical quality ratings can differ significantly in interaction costs and that subjective user ratings are not reliable for measuring productivity.
This paper evaluates whether LLM-generated cyberbullying dialogues faithfully reproduce the social dynamics of authentic interactions, finding that while high-level structures are preserved, finer-grained details are systematically distorted in a model-dependent manner.
This paper proposes TamGraph, a target-aware memory graph method that dynamically selects relevant historical conversations using an entropy-guided mechanism to enhance LLM performance in conversational stance detection tasks.
This study examines how coded dialogues from GenAI virtual patients can provide teacher-interpretable process evidence of clinical reasoning in medical education, using learning analytics to analyze dialogue logs from medical students.
Researchers use three open-source LLMs to annotate 10,600 persuader turns in the PersuasionForGood corpus with 41 persuasion strategies, finding that strategy categories explain little donation variance and guilt induction significantly lowers donation rates.
This paper introduces CIG (Conversational Information Gain), a framework for measuring how utterances advance collective understanding in deliberative dialogues by tracking evolving semantic memory and scoring utterances on novelty, relevance, and implication scope. The authors demonstrate that memory-derived dynamics correlate better with human-perceived dialogue quality than traditional heuristics and develop LLM-based predictors for information-focused conversation analysis.