基于自适应工人分配的多智能体推理用于立场检测
Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection
AI总结：
提出一种Manager-Worker多智能体框架，通过自适应分配工人智能体进行推理级合成，而非标签级投票，在隐式和上下文依赖的立场检测上显著提升性能。
摘要：
立场检测需要识别作者对目标的态度，通常来自简短文本，其中立场是隐含的、间接的或修辞性的。尽管大型语言模型（LLM）在此任务上表现强劲，但当多种解释可能成立时，单次提示可能脆弱。现有的聚合策略，如多数投票或自一致性，通过组合标签来提高鲁棒性，但丢弃了解决冲突解释所需的中间推理。我们提出了一种用于立场检测的自适应工人分配多智能体推理框架，将聚合从标签级投票转变为推理级合成。该框架采用Manager-Worker架构，其中Manager根据输入复杂度自适应地分配可变数量的Worker智能体。每个Worker从不同角度分析输入，并生成仅推理的解释而不输出立场标签；然后Manager综合这些解释以产生最终预测。我们在SemEval-2016、P-Stance和COVID-19 Stance上使用Llama、Mistral和Gemini评估了所提出的框架。结果表明，该框架在隐式和上下文依赖的立场案例上取得了最大增益，在COVID-19上达到86.07 Macro-F1，在SemEval-2016上达到82.90，同时在更显式的立场数据集（如P-Stance）上保持竞争力。这些发现表明，当仅凭表面线索无法可靠推断立场时，自适应推理级聚合最为有益。
英文摘要：
Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large language models (LLMs) achieve strong performance on this task, single-pass prompting can be brittle when multiple interpretations are plausible. Existing aggregation strategies, such as majority voting or self-consistency, improve robustness by combining labels, but they discard the intermediate reasoning needed to resolve conflicting interpretations. We introduce a multi-agent reasoning framework with adaptive worker allocation for stance detection that shifts aggregation from label-level voting to reasoning-level synthesis. The framework employs a Manager-Worker architecture in which a Manager adaptively allocates a variable number of Worker agents based on input complexity. Each Worker analyzes the input from a distinct perspective and produces a reasoning-only explanation without emitting a stance label; the Manager then synthesizes these explanations to produce the final prediction. We evaluate the proposed framework on SemEval-2016, P-Stance, and COVID-19 Stance using Llama, Mistral, and Gemini. Results show that the framework yields the largest gains on implicit and context-dependent stance cases, achieving 86.07 Macro-F1 on COVID-19 and 82.90 on SemEval-2016, while remaining competitive on more explicit stance datasets such as P-Stance. These findings suggest that adaptive reasoning-level aggregation is most beneficial when stance cannot be reliably inferred from surface cues alone.
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。
