这一任务的目的是align每个graph token和对应的node text information。 Tuning Strategy 为了有效的tuning,作者提出一种Lightweight Alignment Projector策略。 在训练过程中,保持LLM和graph encoder的参数不变,只单独优化projector f_{\mathbf p} (一个单线性层)。我们假设训练后projector已经成功学习到将encoder graph ...
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然而,由于上下文窗口和内存使用的限制,基于 Transformer 的LLM 在处理长文本时仍然面临挑战。 现有方法的局限性 目前,解决 LLM 长文本任务的技术主要分为两类: 模型级方法: 通过修改位置编码或注意力机制来扩展 LLM 的上下文窗口。例如,使用位置插值 (Positional Interpolation) 或 SkipAlign 等技术来训练 LLM,使其...
step2.图谱划分和描述生成 有了图,下一步就是如何描述图谱信息,在大模型之前我们更多是采用模版,来把实体和实体关系信息转化成文本,而在LLM时代有了更多可能。这里微软的特色是多了一步对图谱进行划分和描述。 图谱划分,也叫社群发现,之所以要做社群发现,其实就来自GraphRag要解决全局主体,汇总类,关联类的问题,而...
3. GNN-LLM Alignment This category focuses specifically on techniques to align the vector spaces of GNN and LLM encoders for improved consolidated reasoning, while retaining their specialized roles. Symmetric Alignment Methods like contrastive representation learning given aligned graph-text ...
PyTorch implementation for Breaking Information Cocoons: A Hyperbolic Graph-LLM Framework for Exploration and Exploitation in Recommender SystemsBreaking Information Cocoons: A Hyperbolic Graph-LLM Framework for Exploration and Exploitation in Recommender Systems Qiyao Ma, Menglin Yang, Mingxuan Ju, Tong Zhao...
processing tasks. However, their integration with graph structures, which are prevalent in real-world applications, remains relatively unexplored. This repository aims to bridge that gap by providing a curated list of research papers that explore the intersection of graph-based techniques with LLMs. ...
LLM caching mechanism to save as much cost as possible when re-indexing, so re-runs over a dataset are often significantly faster and cheaper than an initial run. Adding brand new content can alter the community structure such that much of an index needs to be re-computed –...
Aligning LLMs with explicit, structured knowledge from KGs has been a challenge; previous attempts either failed to effectively align knowledge representations or compromised the generative capabilities of LLMs, leading to less-than-optimal outcomes. This paper proposes \textbf{KaLM}, a \textit{...
• This work aims to align graph domain-specific structural knowl-edge with the reasoning ability of Large Language Models (LLMs)to improve the generalization of graph learning. -这项工作旨在将特定图领域的结构知识与大型语言模型(LLM)的推理能力结合起来,以提高图学习的泛化能力。