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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.