Our goal is a modern approach to answering questions via systematic reasoning where answers are supported by human interpretable proof trees grounded in an NL corpus of authoritative facts. Such a system would help alleviate the challenges of interpretability and hallucination with modern LMs, and the lack of grounding of current explanation methods (e.g., Chain-of-Thought). This paper proposes a new take on Prolog-based inference engines, where we replace handcrafted rules with a combination of neural language modeling, guided generation, and semiparametric dense retrieval. Our implementation, NELLIE, is the first system to demonstrate fully interpretable, end-to-end grounded QA as entailment tree proof search, going beyond earlier work explaining known-to-be-true facts from text. In experiments, NELLIE outperforms a similar-sized state-of-the-art reasoner [Tafjord et al., 2022] while producing knowledge-grounded explanations. We also find NELLIE can exploit both semi-structured and NL text corpora to guide reasoning. Together these suggest a new way to jointly reap the benefits of both modern neural methods and traditional symbolic reasoning.

翻译：我们的目标是提出一种现代问答方法，通过系统化推理得出答案，且答案需由基于人类可理解的证明树支撑，这些证明树根植于包含权威事实的自然语言语料库。此类系统有助于缓解现代语言模型在可解释性与幻觉生成方面的挑战，并弥补当前解释方法（如思维链）缺乏事实依据的缺陷。本文提出了一种基于Prolog推理引擎的新思路，用神经语言建模、引导式生成与半参数化密集检索的组合取代手工规则。我们的实现系统NELLIE是首个展示完全可解释、端到端基于事实的问答（作为蕴含树证明搜索）的系统，超越了先前仅对文本中已知事实进行解释的研究。实验表明，NELLIE在生成基于知识解释的同时，优于同等规模的先进推理器[Tafjord et al., 2022]。我们还发现NELLIE能利用半结构化与非结构化文本语料库来引导推理。这些结果共同为融合现代神经方法与传统符号推理的优势提供了新途径。