M2CL trains per-agent context generators with a self-adaptive mechanism to maintain coherence and reduce output discrepancies in multi-LLM discussions, yielding 20-50% gains on reasoning, embodied, and mobile control tasks.
Llm discussion: Enhancing the creativity of large language models via discussion framework and role-play,
3 Pith papers cite this work. Polarity classification is still indexing.
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MAGIC-HMO is a multi-agent framework that treats Chinese short-form creative NLG as heterogeneous multi-objective optimization over personalized constraints plus explanation reliability and outperforms baselines on a baby-naming benchmark.
Generates 550 roles and 33,000 questions to evaluate 10 LLMs in role-playing, finding 107,580 biased responses.
citing papers explorer
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Context Learning for Multi-Agent Discussion
M2CL trains per-agent context generators with a self-adaptive mechanism to maintain coherence and reduce output discrepancies in multi-LLM discussions, yielding 20-50% gains on reasoning, embodied, and mobile control tasks.
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Chinese Short-Form Creative Content Generation via Explanation-Oriented Multi-Objective Optimization
MAGIC-HMO is a multi-agent framework that treats Chinese short-form creative NLG as heterogeneous multi-objective optimization over personalized constraints plus explanation reliability and outperforms baselines on a baby-naming benchmark.
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Fairness Testing of Large Language Models in Role-Playing
Generates 550 roles and 33,000 questions to evaluate 10 LLMs in role-playing, finding 107,580 biased responses.