David Silver really hits it out of the park in this podcast. The paper "Welcome to the Era of Experience" is here: https://t.co/Y6m4jLRjnh.

🚀 Introducing ReCall, learning to Reason with Tool Call via RL. - Multi-turn Reinforcement Learning - No need for supervised data on tool use or reasoning steps - Empowers LLMs to agentically use and combine arbitrary tools Fully open-source! A work in progress and we are

🌟Introducing 𝗥𝗲𝗦𝗲𝗮𝗿𝗰𝗵: Learning to Reason with Search for LLMs via Reinforcement Learning. An open-source project that combines 𝗥𝗟 and 𝗥𝗔𝗚 for LLMs! 💡Like Deepseek-R1-Zero and Deep Research, we start with pretrained models and use RL to empower them with the

We are thinking about the possibility of synthesize instruction data for finetuning LLMs. In this #EMNLP2024 Findings work. We utilize the complex graph patterns in KGs to automatically generate plan of a question, and utilize the planning data to finetune LLMs. This works well.

I like this work pretty much. We are trying to explore realistic settings for automatic knowledge graph completion. We also tried to use LLM for the Triple Set Prediction (TSP) task. Empirical study results show that TSP is not an easy task for LLM. See

Please check our work that will be published at NLPCC 2023 for more discussion: MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion https://t.co/5aNmm81dCu

We always find that some of the modal data are missing in multi-modal KGs. The missing modality information undermine the model’s performances during completion. We find generating missing modality features and a cross-modal contrastive loss helps.

#IJCAI2023 our comprehensive survey paper on "Knowledge Extrapolation", the capability of handling unseen entities or new relations in KGs. "Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs" https://t.co/t96E445Ooo

Looking forward to meet you at #IJCAI2023 and welcome to our tutorial - the 2nd edition of K-ZSL tutorial. Check 👇for details.

Do you want to know how to pre-train a knowledge graph model on a KG and apply it on other tasks supported by different KGs in a uniform way? Check our work KGTransformer accepted by #TheWebConf 2023. https://t.co/ohVLlCAQ0z

Our work "Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding" accepted by #AAAI23 is available online ;)

The source code of AnKGE(#AAAI2023) based on #NeuralKG is available at

We developed a toolkit for diverse representation learning of knowledge graphs, called #NeuralKG. It includes diverse Conventional KGEs, GNN-based KGEs, and Rule-based KGEs. Yesterday we added a recently proposed GNN-KGE method SE-GNN. Check it on github

Our paper titled "Analogical Inference Enhanced Knowledge Graph Embedding" accepted by #AAAI23 is available online. In this work, we propose AnKGE, an enhanced KGE framework that enable KGEs with analogical inference capability. Check our paper 👉 https://t.co/H019HFIqV8

Our KG-based ZSL work "Disentangled Ontology Embedding for Zero-shot" https://t.co/ZpfpXHnggk accepted by KDD'22, by @GengYuxia @ChenJiaoyan1 @wencolani @ZhuoChe56641253 @jpansw @ChenHuajun etc. #KnowledgeGraph @kdd_news #KDD2022

~2 days to submit an idea for Hybrid 21st #iswc_conf #iswc2022 Workshops & Tutorials! This could be either sharing a new technology or having great minds come together for intense scientific exchange on a specific topic in the field! Conference Website: https://t.co/v5ntpVAPqU

Join us and share your research with the community through the track that fits best for your work! Joint CF Resource Track, In-Use Track, Research Track Papers #iswc_conf #iswc_2022 Conference Website: https://t.co/v5ntpVAPqU

A new survey and perspective paper on "Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective", by @wencolani @ChenJiaoyan1 @jpansw @ChenHuajun etc. https://t.co/iWNzkk1niY #KnowledgeGraph #NeuralSymbolic #AI #DeepLearning #Reasoning