Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases. We attribute this challenge to the uneven memory performance of LLMs across different positions in graph description sequences, known as ''positional biases''. To address this, we propose GraphInsight, a novel framework aimed at improving LLMs' comprehension of both macro- and micro-level graphical information. GraphInsight is grounded in two key strategies: 1) placing critical graphical information in positions where LLMs exhibit stronger memory performance, and 2) investigating a lightweight external knowledge base for regions with weaker memory performance, inspired by retrieval-augmented generation (RAG). Moreover, GraphInsight explores integrating these two strategies into LLM agent processes for composite graph tasks that require multi-step reasoning. Extensive empirical studies on benchmarks with a wide range of evaluation tasks show that GraphInsight significantly outperforms all other graph description methods (e.g., prompting techniques and reordering strategies) in understanding graph structures of varying sizes.
Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
Michigan State University（密歇根州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；MIT-IBM Watson AI Lab, IBM Research（麻省理工学院-IBM沃森人工智能实验室，IBM研究院）
Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code
The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Unlocking In-Context Learning for Natural Datasets Beyond Language Modelling
Computer Vision Group, University of Freiburg（弗赖堡大学计算机视觉组）；Neuromedical A.I. Lab, Medical Center -- University of Freiburg（弗赖堡大学医学神经AI实验室）；Medical Physics, Medical Center -- University of Freiburg（弗赖堡大学医学物理）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
Rashid: A Cipher-Based Framework for Exploring In-Context Language Learning
Johns Hopkins University, Center for Language and Speech Processing（约翰霍普金斯大学语言与语音处理中心）；MaiNLP, Center for Information and Language Processing, LMU Munich（MaiNLP语言信息处理中心，慕尼黑大学）
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
BNU-HKBU United International College（北师港浸大）；Hong Kong Baptist University（香港浸会大学）；Beijing Normal University at Zhuhai（北京师范大学珠海校区）；The Chinese University of Hong Kong（香港中文大学）
Are Large Language Models the New Interface for Data Pipelines?
University of Trieste（的里雅斯特大学）；University of Milan（米兰大学）；University of Lübeck（吕贝克大学）；Birzeit University（比尔宰特大学）；IRIT（信息学、机器人与信息技术研究所）；University Toulouse 3（图卢兹第三大学）；Université Paris Cité（巴黎城市大学）；University of Twente（特温特大学）
