Despite readily memorizing world knowledge about entities, pre-trained
language models (LMs) struggle to compose together two or more facts to perform
multi-hop reasoning in question-answering tasks. In this work, we propose
techniques that improve upon this limitation by relying on random walks over
structured knowledge graphs. Specifically, we use soft prompts to guide LMs to
chain together their encoded knowledge by learning to map multi-hop questions
to random walk paths that lead to the answer. Applying our methods on two T5
LMs shows substantial improvements over standard tuning approaches in answering
questions that require 2-hop reasoning.

本文提出通过结构化知识图谱中的随机游走来指导语言模型完成多跳推理，从而有效解决语言模型在问答任务中组合多条逻辑事实的限制，并在两个 T5 语言模型上取得了显著的实验效果提升。

使用软提示和随机游走触发语言模型中的多跳推理以进行问答

Triggering Multi-Hop Reasoning for Question Answering in Language Models  using Soft Prompts and Random Walks

In this paper, we study the problem of parsing structured knowledge graphs
from textual descriptions. In particular, we consider the scene graph
representation that considers objects together with their attributes and
relations: this representation has been proved useful across a variety of
vision and language applications. We begin by introducing an alternative but
equivalent edge-centric view of scene graphs that connect to dependency parses.
Together with a careful redesign of label and action space, we combine the
two-stage pipeline used in prior work (generic dependency parsing followed by
simple post-processing) into one, enabling end-to-end training. The scene
graphs generated by our learned neural dependency parser achieve an F-score
similarity of 49.67% to ground truth graphs on our evaluation set, surpassing
best previous approaches by 5%. We further demonstrate the effectiveness of our
learned parser on image retrieval applications.

本文介绍了一种通过神经依赖解析器直接从文本描述中解析出场景图来实现端到端训练的方法，并在评估数据集上比以往方法高出 5％的 F - 分数相似度来生成了我们学习的神经依赖解析器产生的场景图。