2024年6月26日 · 10月2号刚刚公布的论文“Reasoning On Graphs: Faithful And Interpretable Large Language Model Reasoning“，来自澳大利亚的两所大学。 大语言模型（LLM）在复杂任务中表现出了令人印象深刻的推理能力。

2023年10月2日 · In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning. Specifically, we present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans.

Reasoning on graphs (RoG) synergizes LLMs with KGs to enable faithful and interpretable reasoning. We present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans.

2024年3月9日 · 本文在分析了现有文献的基础上，提出了一种称为图上推理（RoG）的新方法，该方法使LLM与KGs协同工作，实现忠实和可解释的推理。 具体来说，本文提出了一个 规划-检索-推理框架，其中RoG首先生成以KGs落地的关系路径作为忠实的规划。 然后，这些规划被用于从KG中检索有效的推理路径，以便LLM进行忠实的推理。 此外，RoG不仅从KGs中提取知识，通过训练提高LLM的推理能力，其允许在推理过程中与任意LLM做到无缝集成。 在两个基准KGQA …

Key research questions: (1) how can we enable precise visual perception in LMMs? and (2) how can we facilitate high-level reasoning based on such low-level perceptions? Method: To accurately capture low-level visual details, we utilize Scalable Vector Graphics (SVG) for precise encoding of visual scenes. However, SVGs are not readily ...

2024年10月3日 · We present GraphIC, a novel approach that leverages graph-based representations of reasoning processes, coupled with Bayesian Networks (BNs) to select ICEs. Graph structures inherently filter out shallow semantics while …

vector graphics—images composed purely of 2D objects and shapes, which are prevalent in various LMM-based agent tasks in web, visual design, and OS environ-ments. We identify two key research questions: how can we enable precise visual perception, and how can we facilitate high-level reasoning based on such low-level perceptions?

2023年5月26日 · In chart de-rendering, given a plot or chart, the image-to-text model is required to generate its underlying data table or the code used to render it. For math reasoning pre-training, we pick textual numerical reasoning datasets and render the input into images, which the image-to-text model needs to decode for answers.

Let’s do multiple dynamically–built graphs! Aren’t their structures and relations important too? We got sets and graphs, how about sequences? Videos pose another challenge for visual reasoning: the dynamics through time. Sets and graphs now becomes sequences of such.

geometric models to perform visual reasoning. Qualitative results provide valuable insights to understanding of a domain. Graphs represent qualitative information effectively by presenting it in a spatial format that our perceptual mechanism can process easily. SKETCHY qualitatively describes line slopes and curvatures. A straight line ...