Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration | Paper Citation Record

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This paper cites Integrating generative AI into enterprise platforms: Insights from salesforce.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Integrating generative AI into enterprise platforms: Insights from salesforce

Reference 1

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Industrial applications of large language models

Reference 2

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Large language model routing with benchmark datasets

Reference 3

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Routing to the expert: Efficient reward-guided ensemble of large language models

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Enterprise generative AI : 10+ use cases & best practices, 2024

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration The Wisdom of Crowds

Reference 6

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This paper cites Combining crowd and machine intelligence to detect false news on social media.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Combining crowd and machine intelligence to detect false news on social media

Reference 7

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Distilling the wisdom of crowds: Prediction markets vs

Reference 8

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Human-algorithm collaborative truth inference in crowdsourcing

Reference 9

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration The crowd classification problem: Social dynamics of binary-choice accuracy

Reference 10

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Model-based wisdom of the crowd for sequential decision-making tasks

Reference 11

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Chain-of-thought prompting elicits reasoning in large language models

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Self-Refine : Iterative refinement with self-feedback

Reference 13

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Reflexion: Language agents with verbal reinforcement learning

Reference 14

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Self-consistency improves chain of thought reasoning in language models

Reference 15

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration LLM-Blender : Ensembling large language models with pairwise ranking and generative fusion

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This paper cites A survey on LLM -based multi-agent systems: Workflow , infrastructure, and challenges.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration A survey on LLM -based multi-agent systems: Workflow , infrastructure, and challenges

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Large language model based multi-agents: A survey of progress and challenges

Reference 18

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This paper cites English dictionary, 2025.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration English dictionary, 2025

Reference 19

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This paper cites Learning from crowdsourced multi-labeling: A variational bayesian approach.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Learning from crowdsourced multi-labeling: A variational bayesian approach

Reference 20

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration u hl, Michael V \

Reference 21

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This paper cites Does the whole exceed its parts? The effect of AI explanations on complementary team performance.

Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Does the whole exceed its parts? The effect of AI explanations on complementary team performance

Reference 22

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Design science in information systems research

Reference 23

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Pathways for design research on artificial intelligence

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Computational design science: A critical information systems research area contributing to artificial intelligence and data science

Reference 25

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration When can LLMs actually correct their own mistakes? A critical survey of self-correction of LLMs

Reference 26

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration What makes reasoning invalid: Echo reflection mitigation for large language models

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration A contextual-bandit approach to personalized news article recommendation

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration u gener, J \

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Human-algorithm collaboration: Achieving complementarity and avoiding unfairness

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Bayesian modeling of human-- AI complementarity

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Ensemble methods in machine learning

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Ensemble learning: A survey

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Complexity-based prompting for multi-step reasoning

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Making language models better reasoners with step-aware verifier

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Just ask one more time! Self -agreement improves reasoning of language models in (almost) all scenarios

Reference 36

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Examining inter-consistency of large language models collaboration: An in-depth analysis via debate

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration MetaGPT : Meta programming for a multi-agent collaborative framework

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Chatdev: Communicative agents for software development

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration ChatGPT research group for optimizing the crystallinity of MOFs and COFs

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration MedAgents : Large language models as collaborators for zero-shot medical reasoning

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Improving factuality and reasoning in language models through multiagent debate

Reference 42

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Social simulacra: Creating populated prototypes for social computing systems

Reference 43

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Some aspects of the sequential design of experiments

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Reinforcement learning: An introduction

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Finite-time analysis of the multiarmed bandit problem

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Spearman’s rank correlation coefficient

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Measuring massive multitask language understanding

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration VisEval : A benchmark for data visualization in the era of large language models

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration ReAct : Synergizing reasoning and acting in language models

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Universal self-consistency for large language models

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration AWQ : Activation-aware weight quantization for on-device LLM compression and acceleration

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration LiveBench: A Challenging, Contamination-Limited LLM Benchmark

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Nomic embed: Training a reproducible long context text embedder

Reference 57

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration LLMRouter : An open-source library for LLM routing

Reference 58

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Router-R1 : Teaching LLMs multi-round routing and aggregation via reinforcement learning

Reference 59

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration RouteLLM : Learning to route LLMs from preference data

Reference 60

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration GraphRouter : A graph-based router for LLM selections

Reference 61

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration ICML Workshop on In-Context Learning , year=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Journal of Operations Research , volume =

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Annual Conference on Neural Information Processing Systems , year=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Routing to the Expert:

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Large Language Models Cannot Self-Correct Reasoning Yet

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration When can

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Distilling the wisdom of crowds:

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration The crowd classification problem:

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Cognitive Science , volume=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Some aspects of the sequential design of experiments , journal=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Reinforcement learning:

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Proceedings of the 19th International Conference on World Wide Web , pages=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration International workshop on multiple classifier systems , pages=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Ensemble learning:

Reference 86

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration The Curious Case of Neural Text Degeneration

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Findings of the Association for Computational Linguistics: NAACL 2024 , pages=

Reference 88

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Purifying Large Language Models by Ensembling a Small Language Model

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration The Twelfth International Conference on Learning Representations , year=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Bridging the Gap between Different Vocabularies for

Reference 92

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Advances in Neural Information Processing Systems , volume=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration What Makes Reasoning Invalid:

Reference 94

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration The Eleventh International Conference on Learning Representations , year=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Learning from crowdsourced multi-labeling:

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Advances in Neural Information Processing Systems , volume=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration 11th International Conference on Learning Representations, ICLR 2023 , year=

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 99

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Just Ask One More Time!

Reference 100

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Complementarity in human-

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Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration Cognitive challenges in human--artificial intelligence collaboration:

Reference 103

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