优化我们信任的内容：基于可靠性引导的多智能体弱标注信号QUBO选择用于阿拉伯语情感预测
Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction
AI总结：
本文提出一种基于可靠性的弱监督框架，通过QUBO选择优化多智能体弱标注信号，提升阿拉伯语情感预测的可靠性与结构迁移能力。
摘要：
在阿拉伯社交媒体中进行框架检测具有挑战性，因为存在解释性模糊、文化基础和有限可靠的监督。现有的基于LLM的弱监督方法通常依赖于标签聚合，当标注很少且具有社会依赖性时，这种方法显得脆弱。我们提出了一种可靠性意识的弱监督框架，将重点从标签融合转移到数据整理。一个小型的多智能体LLM流水线、两个框架检测器、一个批评者和一个判别器，将分歧和推理质量视为知识信号，并产生实例级可靠性估计。这些估计指导基于QUBO的子集选择过程，以在保持框架平衡的同时减少冗余。内在诊断和一个跨域的阿拉伯情感迁移测试显示，所选子集更加可靠，编码了非随机、可迁移的结构，而不会降低强文本-only基线。
英文摘要：
Framing detection in Arabic social media is difficult due to interpretive ambiguity, cultural grounding, and limited reliable supervision. Existing LLM-based weak supervision methods typically rely on label aggregation, which is brittle when annotations are few and socially dependent. We propose a reliability-aware weak supervision framework that shifts the focus from label fusion to data curation. A small multi-agent LLM pipeline, two framers, a critic, and a discriminator, treats disagreement and reasoning quality as epistemic signals and produces instance-level reliability estimates. These estimates guide a QUBO-based subset selection procedure that enforces frame balance while reducing redundancy. Intrinsic diagnostics and an out-of-domain Arabic sentiment transfer test show that the selected subsets are more reliable and encode non-random, transferable structure, without degrading strong text-only baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
