LLM增强的符号NLU系统用于更可靠的连续因果语句解释
LLM-Augmented Symbolic NLU System for More Reliable Continuous Causal Statement Interpretation
AI总结：
本文提出一种混合方法，结合LLM的广泛覆盖与符号NLU的结构化表示，用于从常识科学文本中提取和解释数量及因果规律，实验表明该方法显著优于仅符号流程。
摘要：
尽管大型语言模型（LLMs）具有广泛的适用性，但其对概率推理的依赖使其容易产生错误，例如在生成事实中出现幻觉，以及在自然语言理解（NLU）任务中输出结构不一致。相比之下，符号NLU系统基于精选的词典、语义资源以及句法和语义解释规则，提供可解释的理解。它们产生的关系表示可用于准确的推理和规划，以及增量的可调试学习。然而，符号NLU系统在覆盖范围上往往比LLMs更有限，并且需要稀缺的知识表示和语言学技能来扩展和维护。本文探索了一种混合方法，将LLMs的广泛覆盖语言处理能力与符号NLU产生结构化关系表示的能力相结合，以期获得两种方法的最佳效果。我们使用LLMs进行改写和文本简化，以提供广泛覆盖，并作为信息源更自动地填补知识空白。我们使用符号NLU来产生可用于推理和增量学习的表示。我们在从常识科学文本中提取和解释数量及因果规律的任务上评估了这种方法，并与仅使用符号和仅使用LLM的流程进行了比较。我们的结果表明，我们的混合方法显著优于仅使用符号的流程。
英文摘要：
Despite the broad applicability of large language models (LLMs), their reliance on probabilistic inference makes them vulnerable to errors such as hallucination in generated facts and inconsistent output structure in natural language understanding (NLU) tasks. By contrast, symbolic NLU systems provide interpretable understanding grounded in curated lexicons, semantic resources, and syntactic & semantic interpretation rules. They produce relational representations that can be used for accurate reasoning and planning, as well as incremental debuggable learning. However, symbolic NLU systems tend to be more limited in coverage than LLMs and require scarce knowledge representation and linguistics skills to extend and maintain. This paper explores a hybrid approach that integrates the broad-coverage language processing of LLMs with the symbolic NLU capabilities of producing structured relational representations to hopefully get the best of both approaches. We use LLMs for rephrasing and text simplification, to provide broad coverage, and as a source of information to fill in knowledge gaps more automatically. We use symbolic NLU to produce representations that can be used for reasoning and for incremental learning. We evaluate this approach on the task of extracting and interpreting quantities and causal laws from commonsense science texts, along with symbolic- and LLM-only pipelines. Our results suggest that our hybrid method works significantly better than the symbolic-only pipeline.
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。
