Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences | Paper Citation Record

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This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Large Language Model based Multi-Agents: A Survey of Progress and Challenges

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This paper cites ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Reference 2

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This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Improving Factuality and Reasoning in Language Models through Multiagent Debate

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This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

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This paper cites (eds.) SHELF: The Sheffield Elicitation Framework, pp.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences (eds.) SHELF: The Sheffield Elicitation Framework, pp

Reference 5

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This paper cites Annals of Operations Research 154(1), 51–68 (2007) https://doi.org/10.1007/ s10479-007-0183-3.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Annals of Operations Research 154(1), 51–68 (2007) https://doi.org/10.1007/ s10479-007-0183-3

Reference 6

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This paper cites In: IEE Colloquium on CSCW: Some Fundamental Issues, pp.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: IEE Colloquium on CSCW: Some Fundamental Issues, pp

Reference 7

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This paper cites Autonomous Agents and Multi-Agent Systems 27, 52–84 (2013) https://doi.org/10.1007/ s10458-012-9201-1.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Autonomous Agents and Multi-Agent Systems 27, 52–84 (2013) https://doi.org/10.1007/ s10458-012-9201-1

Reference 8

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This paper cites The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents

Reference 9

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This paper cites In: Proceed- ings of the Annual Meeting of the Cognitive Science Society, vol.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Proceed- ings of the Annual Meeting of the Cognitive Science Society, vol

Reference 10

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This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 11

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This paper cites Frontiers of Computer Science 18(6), 186345 (2024) https://doi.org/10.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Frontiers of Computer Science 18(6), 186345 (2024) https://doi.org/10

Reference 12

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This paper cites In: Larson, K.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Larson, K

Reference 13

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: The 10th International Conf

Reference 15

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences ChatDev: Communicative Agents for Software Development

Reference 16

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This paper cites Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 17

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System

Reference 18

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Social Simulacra: Creating Populated Prototypes for Social Computing Systems

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This paper cites Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research

Reference 20

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars

Reference 21

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Determinants of LLM-assisted Decision-Making

Reference 22

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Fourth Workshop on Knowledge-infused Learning (2024)

Reference 23

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This paper cites Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation

Reference 24

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This paper cites In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp

Reference 25

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Multi-Agent Consensus Seeking via Large Language Models

Reference 26

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 27

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Prompting Large Language Models With the Socratic Method

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This paper cites Advances in Decision Analysis, 375–399 (2007) https://doi.org/10.1017/cbo9780511611308.020.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Advances in Decision Analysis, 375–399 (2007) https://doi.org/10.1017/cbo9780511611308.020

Reference 29

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Journal of Convention & Event Tourism, vol

Reference 30

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This paper cites Social Work in Health Care 27(3), 57–74 (1998) https://doi.org/10.1300/j010v27n03 04.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Social Work in Health Care 27(3), 57–74 (1998) https://doi.org/10.1300/j010v27n03 04

Reference 31

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences (eds.) Decision-Making: Overview, pp

Reference 32

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 33

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Stance Detection on Social Media with Fine-Tuned Large Language Models

Reference 34

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Bethard, S., Carpuat, M., Cer, D., Jurgens, D., Nakov, P., Zesch, T

Reference 35

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Zong, C., Xia, F., Li, W., Navigli, R

Reference 36

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Webber, B., Cohn, T., He, Y., Liu, Y

Reference 37

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Lapata, M., Blunsom, P., Koller, A

Reference 38

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences In: Ku, L.-W., Martins, A., Srikumar, V

Reference 39

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Can Large Language Models Address Open-Target Stance Detection?

Reference 40

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This paper cites International Journal of Drug Policy 56, 144–152 (2018).

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences International Journal of Drug Policy 56, 144–152 (2018)

Reference 41

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Gemma 2: Improving Open Language Models at a Practical Size

Reference 42

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences https://doi.org/10.48550/arXiv.2403

Reference 43

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Mixtral of Experts

Reference 44

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences The Llama 3 Herd of Models

Reference 45

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences https://platform.openai.com/docs/models/ gpt-3-5-turbo

Reference 46

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Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences https://platform.openai.com/docs/ models/gpt-4-turbo-and-gpt-4

Reference 47

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