Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction
arXiv:2603.04416v1 Announce Type: new Abstract: 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.
arXiv:2603.04416v1 Announce Type: new Abstract: 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.
Executive Summary
The article proposes a reliability-aware weak supervision framework for Arabic sentiment prediction, utilizing a multi-agent pipeline to estimate instance-level reliability and guide QUBO-based subset selection. This approach aims to improve the robustness of existing LLM-based methods, which often rely on label aggregation and are brittle with limited annotations. The framework demonstrates promising results in intrinsic diagnostics and out-of-domain sentiment transfer tests, showcasing its potential for reliable and transferable sentiment prediction.
Key Points
- ▸ Reliability-aware weak supervision framework for Arabic sentiment prediction
- ▸ Multi-agent LLM pipeline for estimating instance-level reliability
- ▸ QUBO-based subset selection for enforcing frame balance and reducing redundancy
Merits
Improved Robustness
The proposed framework demonstrates improved robustness compared to existing LLM-based methods, particularly in scenarios with limited annotations.
Transferable Structure
The selected subsets encode non-random, transferable structure, enabling effective sentiment prediction across different domains.
Demerits
Complexity
The multi-agent pipeline and QUBO-based subset selection may introduce additional complexity, potentially increasing computational requirements and implementation challenges.
Dependence on Reliability Estimates
The framework's performance relies heavily on the accuracy of instance-level reliability estimates, which may be affected by various factors, including data quality and annotator bias.
Expert Commentary
The proposed reliability-aware weak supervision framework represents a significant step forward in addressing the challenges of Arabic sentiment prediction. By shifting the focus from label fusion to data curation and leveraging instance-level reliability estimates, the framework demonstrates improved robustness and transferability. However, further research is needed to address the potential complexity and dependence on reliability estimates, as well as to explore the framework's applicability to other languages and domains. The article's emphasis on explainability and transparency also highlights the importance of developing more interpretable sentiment prediction models.
Recommendations
- ✓ Further evaluation of the framework's performance across different languages and domains
- ✓ Investigation into methods for reducing complexity and improving the accuracy of reliability estimates
- ✓ Exploration of the framework's potential applications in social media monitoring and content moderation