11 citations · 19 across the 14 of their papers we have counts for
14 papers
FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs
Jiaxin Pan, Mojtaba Nayyeri, Osama Mohammed +4
Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling
Yitian Chen, Cheng Cheng, Yinan Sun +2
We investigate the capabilities and scalability of Large Language Models (LLMs) in optimization modeling, a domain requiring structured reasoning and precise formulation. To this e…
Multi-Value Alignment for LLMs via Value Decorrelation and Extrapolation
Hefei Xu, Le Wu, Chen Cheng +1
With the rapid advancement of large language models (LLMs), aligning them with human values for safety and ethics has become a critical challenge. This problem is especially challe…
Towards Foundation Model on Temporal Knowledge Graph Reasoning
Jiaxin Pan, Mojtaba Nayyeri, Osama Mohammed +4
Temporal Knowledge Graphs (TKGs) store temporal facts with quadruple formats (s, p, o, t). Existing Temporal Knowledge Graph Embedding (TKGE) models perform link prediction tasks i…
Skywork Open Reasoner 1 Technical Report
Jujie He, Jiacai Liu, Chris Yuhao Liu +14
The success of DeepSeek-R1 underscores the significant role of reinforcement learning (RL) in enhancing the reasoning capabilities of large language models (LLMs). In this work, we…
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
Tiankai Yang, Junjun Liu, Wingchun Siu +6
Anomaly detection (AD) is essential in areas such as fraud detection, network monitoring, and scientific research. However, the diversity of data modalities and the increasing numb…
A Comparative Study of Large Language Models and Human Personality Traits
Wang Jiaqi, Wang bo, Guo fa +2
Large Language Models (LLMs) have demonstrated human-like capabilities in language comprehension and generation, becoming active participants in social and cognitive domains. This…
Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On
Liang Zeng, Liangjun Zhong, Liang Zhao +9
In this paper, we investigate the underlying factors that potentially enhance the mathematical reasoning capabilities of large language models (LLMs). We argue that the data scalin…
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
Tianwen Wei, Bo Zhu, Liang Zhao +13
In this technical report, we introduce the training methodologies implemented in the development of Skywork-MoE, a high-performance mixture-of-experts (MoE) large language model (L…
LongSkywork: A Training Recipe for Efficiently Extending Context Length in Large Language Models
Liang Zhao, Tianwen Wei, Liang Zeng +12
We introduce LongSkywork, a long-context Large Language Model (LLM) capable of processing up to 200,000 tokens. We provide a training recipe for efficiently extending context lengt…
Skywork: A More Open Bilingual Foundation Model
Tianwen Wei, Liang Zhao, Lichang Zhang +27
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts…
Learning from Red Teaming: Gender Bias Provocation and Mitigation in Large Language Models
Hsuan Su, Cheng-Chu Cheng, Hua Farn +4
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4. These LLM-based chatbots…
Graph Propagation Transformer for Graph Representation Learning
Zhe Chen, Hao Tan, Tao Wang +5
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes…