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LLM-Based Zero-Shot Soft Labeling for Anticipating Disagreement in Negotiation Dialogues
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
Negotiation involves complex emotional and strategic dynamics that pose challenges for AI agents in negotiation dialogues. This paper proposes a zero-shot soft-labeling method using an large language model-based embedding model and verifies its performance on negotiation dialogues. Furthermore, it examines the performance of predictive model training on rule-based annotated hard and soft labels obtained by the proposed method for the task of predicting whether agreement will be reached from partial dialogues, namely, final disagreement anticipation in negotiation mid-dialogues (FDANMD). Soft labeling obtained by the proposed method showed a maximum HIT@3 score of 0.87 against rule-based annotated hard labels, whereas failure cases also demonstrated the limitations of rule-based annotation. Furthermore, using ROC AUC, evaluations of FDANMD across three datasets (CB, DN, and JI) with negotiation progress rates of 0.25, 0.5, and 1.0 revealed that soft labeling is particularly effective at low negotiation progress rates and also offers superior performance on individual datasets and unseen datasets for models trained on multiple datasets. These results motivate the use of soft labeling to incorporate the complexity of negotiation dialogues into intermediate representations and support the generalizability of zero-shot soft labeling and generalizable predictors across a wide range of negotiations beyond known domains.
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记录 #1 · 2026-07 · 最新观测版本
LLM-Based Zero-Shot Soft Labeling for Anticipating Disagreement in Negotiation Dialogues
Negotiation involves complex emotional and strategic dynamics that pose challenges for AI agents in negotiation dialogues. This paper proposes a zero-shot soft-labeling method using an large language model-based embedding model and verifies its performance on negotiation dialogues. Furthermore, it examines the performance of predictive model training on rule-based annotated hard and soft labels obtained by the proposed method for the task of predicting whether agreement will be reached from partial dialogues, namely, final disagreement anticipation in negotiation mid-dialogues (FDANMD). Soft labeling obtained by the proposed method showed a maximum HIT@3 score of 0.87 against rule-based annotated hard labels, whereas failure cases also demonstrated the limitations of rule-based annotation. Furthermore, using ROC AUC, evaluations of FDANMD across three datasets (CB, DN, and JI) with negotiation progress rates of 0.25, 0.5, and 1.0 revealed that soft labeling is particularly effective at low negotiation progress rates and also offers superior performance on individual datasets and unseen datasets for models trained on multiple datasets. These results motivate the use of soft labeling to incorporate the complexity of negotiation dialogues into intermediate representations and support the generalizability of zero-shot soft labeling and generalizable predictors across a wide range of negotiations beyond known domains.
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