English(EN) Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection

多智能体大语言模型研究解耦拓扑与多样性以用于情感检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员探讨了如何在低资源多语言情感检测中解耦多智能体大语言模型（LLMs）的拓扑与多样性。通过独立研究推理拓扑和智能体间多样性的来源，他们发现并行学习的专业化在 Qwen2.5-14B-Instruct 和 Llama-3.1-8B-Instruct 模型上均取得了最佳结果。研究还表明，智能体区分方法对性能的影响比拓扑本身更大，这表明这些因素应联合评估。 AI

影响这项研究可能为低资源多语言情感检测等专业任务带来更有效、更智能的多智能体大语言模型系统。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ulugbek Shernazarov, Charitha Ruwansiri Weerakon Basnayake, Abdelkhaleq El Jarjini, Noel Crespi, Praboda Rajapaksha · 2026-09-15 04:00

解耦多智能体大语言模型中用于多语言低资源情感检测的拓扑与多样性

arXiv:2609.14570v1 Announce Type: cross Abstract: Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We study these factors independently: inference topology and source of inter-agent div…

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解耦多智能体大语言模型中用于多语言低资源情感检测的拓扑与多样性

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