Graphs data is crucial for many applications, and much of it exists in the
relations described in textual format. As a result, being able to accurately
recall and encode a graph described in earlier text is a basic yet pivotal
ability that LLMs need to demonstrate if they are to perform reasoning tasks
that involve graph-structured information. Human performance at graph recall by
has been studied by cognitive scientists for decades, and has been found to
often exhibit certain structural patterns of bias that align with human
handling of social relationships. To date, however, we know little about how
LLMs behave in analogous graph recall tasks: do their recalled graphs also
exhibit certain biased patterns, and if so, how do they compare with humans and
affect other graph reasoning tasks? In this work, we perform the first
systematical study of graph recall by LLMs, investigating the accuracy and
biased microstructures (local structural patterns) in their recall. We find
that LLMs not only underperform often in graph recall, but also tend to favor
more triangles and alternating 2-paths. Moreover, we find that more advanced
LLMs have a striking dependence on the domain that a real-world graph comes
from -- by yielding the best recall accuracy when the graph is narrated in a
language style consistent with its original domain.

图数据、图回忆、LLMs、局部结构偏见以及领域依赖是这篇论文的五个关键词，论文主要研究了 LLMs 在图回忆任务中的准确性、偏见的局部结构模式以及其对其他图推理任务的影响。发现 LLMs 在图回忆任务中表现普遍不佳，且倾向于偏爱三角形和交替 2 路径，同时更高级的 LLMs 对真实世界图的回忆准确性取决于图所属领域，最佳回忆效果出现在与原始领域语言风格一致的图上。

大型语言模型对图形回忆的微观结构和准确性

Microstructures and Accuracy of Graph Recall by Large Language Models

Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph
from textual input. Recently, there has been notable growth in AMR parsing
performance. However, most existing work focuses on improving the performance
in the specific domain, ignoring the potential domain dependence of AMR parsing
systems. To address this, we extensively evaluate five representative AMR
parsers on five domains and analyze challenges to cross-domain AMR parsing. We
observe that challenges to cross-domain AMR parsing mainly arise from the
distribution shift of words and AMR concepts. Based on our observation, we
investigate two approaches to reduce the domain distribution divergence of text
and AMR features, respectively. Experimental results on two out-of-domain test
sets show the superiority of our method.

本研究通过对五个领域的五种具有代表性的抽象意义表示解析器进行广泛评估和分析，旨在解决抽象意义表示解析系统的领域依赖性问题，并通过研究两种方法减少文本和 AMR 特征的领域分布差异，最终实验证明我们的方法的优越性。

跨领域 AMR 解析泛化

Cross-domain Generalization for AMR Parsing

Meaning of a word varies from one domain to another. Despite this important
domain dependence in word semantics, existing word representation learning
methods are bound to a single domain. Given a pair of
\emph{source}-\emph{target} domains, we propose an unsupervised method for
learning domain-specific word representations that accurately capture the
domain-specific aspects of word semantics. First, we select a subset of
frequent words that occur in both domains as \emph{pivots}. Next, we optimize
an objective function that enforces two constraints: (a) for both source and
target domain documents, pivots that appear in a document must accurately
predict the co-occurring non-pivots, and (b) word representations learnt for
pivots must be similar in the two domains. Moreover, we propose a method to
perform domain adaptation using the learnt word representations. Our proposed
method significantly outperforms competitive baselines including the
state-of-the-art domain-insensitive word representations, and reports best
sentiment classification accuracies for all domain-pairs in a benchmark
dataset.

本研究提出了一种无监督方法，学习特定领域的单词表示，以准确捕捉单词语义的领域特定方面，并使用所学习的单词表示进行域适应性处理，以在对多个不同领域对情感分类任务中获得最佳准确性，并显着优于现有基准。