33 citations · 95 across the 19 of their papers we have counts for
11 papers · 1 filter
Large Language Models on Graphs: A Comprehensive Survey
Bowen Jin, Gang Liu, Chi Han +3
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and n…
InfoPattern: Unveiling Information Propagation Patterns in Social Media
Chi Han, Jialiang Xu, Manling Li +3
Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation. Our demo InfoPattern centers on the interpla…
Defining a New NLP Playground
Sha Li, Chi Han, Pengfei Yu +8
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in…
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models
Chi Han, Qifan Wang, Hao Peng +4
Today's large language models (LLMs) typically train on short text segments (e.g., <4K tokens) due to the quadratic complexity of their Transformer architectures. As a result, thei…
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
Chi Han, Qizheng He, Charles Yu +3
Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on…
Understanding the Effect of Data Augmentation on Knowledge Distillation
Ziqi Wang, Chi Han, Wenxuan Bao +1
Knowledge distillation (KD) requires sufficient data to transfer knowledge from large-scale teacher models to small-scale student models. Therefore, data augmentation has been wide…
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models
Cheng Qian, Chi Han, Yi R. Fung +3
Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning, particul…
Word Embeddings Are Steers for Language Models
Chi Han, Jialiang Xu, Manling Li +5
Language models (LMs) automatically learn word embeddings during pre-training on language corpora. Although word embeddings are usually interpreted as feature vectors for individua…
Understanding Emergent In-Context Learning from a Kernel Regression Perspective
Chi Han, Ziqi Wang, Han Zhao +1
Large language models (LLMs) have initiated a paradigm shift in transfer learning. In contrast to the classic pretraining-then-finetuning procedure, in order to use LLMs for downst…
RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought
Tianci Xue, Ziqi Wang, Zhenhailong Wang +3
Large language Models (LLMs) have achieved promising performance on arithmetic reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting. However, LLMs face ch…
Tool Learning with Foundation Models
Yujia Qin, Shengding Hu, Yankai Lin +38
Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models,…