发表机构
IBM Research; IBM（IBM研究院; 国际商业机器公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出混合符号方法，将策略表示为形式逻辑，结合语言模型事实提取与答案集求解器推理，性能优于策略作为提示、策略作为代码方法，令牌用量减少约10倍，助力生成式AI系统做出鲁棒决策。
AI 中文摘要
在生成式AI系统的许多实际应用中，从税务规则到航空公司行李限额，对自然语言查询的响应必须遵守书面政策或规则。我们提出一种混合符号方法，该方法将策略表示为形式逻辑，并在推理时利用语言模型的表示能力进行事实提取以确定谓词，同时使用答案集求解器进行推理，从而使响应具有可解释性、可审计性，且如我们所示，在输入扰动下准确且鲁棒。具体而言，我们表明这种提取与推理步骤的分离在大多数情况下优于策略作为提示（policy-as-prompt）和策略作为代码（policy-as-code）方法，同时令牌使用量减少约10倍。这些结果表明，结合生成式模型的结构化推理和符号求解器对于做出涉及客观标准的鲁棒决策具有重要价值。
英文摘要
In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect written policies or rules. We present a hybrid symbolic approach that expresses policies in formal logic and at inference time exploits the representation power of language models for fact extraction to ground predicates, and an answer set solver for reasoning such that responses are interpretable, auditable, and as we show, accurate and robust under input perturbations. Specifically, we show this separation of extraction and reasoning steps outperforms policy-as-prompt and policy-as-code methods in most cases with ~10x reduction in token usage. The results point to the value of structured reasoning and symbolic solvers in conjunction with generative models to make robust decisions involving objective criteria.
CommentsRobustifAI Workshop at IJCAI-ECAI '26
