让大模型在运行时自动修复多个约束冲突，避免无效循环。
Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime
- 通过监控执行历史识别约束冲突的振荡失败
- 调用元修复模型生成平衡策略指令，打破僵局
- 适用于需要多约束协同的复杂推理任务
语言模型流水线可在运行时根据程序化约束动态优化输出。然而，当面临相互竞争的软约束时，其有效性会崩溃，导致满足一个约束即违反另一个的低效回溯循环。我们提出元自修正框架，为语言模型流水线添加元纠错层，在推理阶段实时修复此类冲突。该方法通过监测流水线执行历史来检测振荡失败；一旦发现，便调用元修复语言模型分析回溯尝试的整体状态，并合成一条战略性指令以平衡相互竞争的需求。该自修复指令引导原始语言模型摆脱失败的优化循环，获得成功输出。实验表明，元自修正可有效修复这些循环，显著提升语言模型程序的效率。
原文摘要 · Abstract (English)
Language Model (LM) pipelines can dynamically refine their outputs against programmatic constraints. However, their effectiveness collapses when faced with competing soft constraints, leading to inefficient backtracking loops where satisfying one constraint violates another. We introduce Meta Self-Refining, a framework that equips LM pipelines with a meta-corrective layer to repair these competitions at runtime/inference-time. Our approach monitors the pipeline's execution history to detect oscillatory failures. Upon detection, it invokes a meta-repairer LM that analyzes the holistic state of the backtracking attempts and synthesizes a strategic instruction to balance the competing requirements. This self-repair instruction guides the original LM out of a failing refining loop towards a successful output. Our results show Meta Self-Refining can successfully repair these loops, leading to more efficient LM programs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。