6 papers · 1 filter
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory
Zihan Chen, Songwei Dong, Chengshuai Shi +4
Sequentially evolving LLM memory enables agents to reuse past experience, but existing systems usually deploy each locally generated memory update without checking whether it impro…
A Survey of Scaling in Large Language Model Reasoning
Zihan Chen, Song Wang, Zhen Tan +6
The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration. How…
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning
Zihan Chen, Song Wang, Xingbo Fu +4
The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. Ho…
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
Zihan Chen, Song Wang, Zhen Tan +2
In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle diverse tasks by incorporating multiple input-output examples, known as demonstrations, into the input of…
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Song Wang, Zhenyu Lei, Zhen Tan +8
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and hum…
KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models
Zaiyi Zheng, Yushun Dong, Song Wang +3
Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classific…