mlsanityData to 14 Oct 2026

A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing

arXiv 2506.21565 ↗First posted 12 Jun 2025cs.CL cs.LG stat.MLcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Japan's kairanban culture and idobata conversations have long functioned as traditional communication practices that foster nuanced dialogue among community members and contribute to the formation of social balance. Inspired by these information exchange processes, this study proposes a multi-agent inference framework (KCS+IBC) that integrates multiple large language models (LLMs) to achieve bias mitigation, improved explainability, and probabilistic prediction in sentiment analysis. In addition to sequentially sharing prediction results, the proposed method incorporates a mid-phase casual dialogue session to blend formal inference with individual perspectives and introduces probabilistic sentiment prediction. Experimental results show that KCS achieves accuracy comparable to that of a single LLM across datasets, while KCS+IBC exhibits a consistent decrease in entropy and a gradual increase in variance during the latter stages of inference, suggesting the framework's ability to balance aggregation and diversity of predictions. Future work will quantitatively assess the impact of these characteristics on bias correction and aim to develop more advanced sentiment analysis systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 21 of its 31 references that are in the map. First posted on arXiv 12 Jun 2025.

Builds on

21 of 31 references.

References · 21

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 688
  3. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  4. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  5. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  6. 2024Large Language Models: A Surveycited by 525
  7. 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 417
  8. 2013Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Textscited by 310
  9. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 232
  10. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 222
  11. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 119
  12. 2017Natural Language Processing: State of The Art, Current Trends and Challengescited by 114
9 more
  1. 2023Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMscited by 62
  2. 2023Prompt Engineering a Prompt Engineercited by 53
  3. 2023ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Modelscited by 43
  4. 2023WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Modelscited by 42
  5. 2023Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agentscited by 41
  6. 2024A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directionscited by 27
  7. 2024Locating and Mitigating Gender Bias in Large Language Modelscited by 12
  8. 2024Injecting Salesperson's Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoningcited by 9
  9. 2022Conceptor-Aided Debiasing of Large Language Modelscited by 5

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  3. 2026Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach
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  8. 2025Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?