22 citations · 41 across the 19 of their papers we have counts for
19 papers
Can Large Language Models Generalize Procedures Across Representations?
Fangru Lin, Valentin Hofmann, Xingchen Wan +4
Large language models (LLMs) are trained and tested extensively on symbolic representations such as code and graphs, yet real-world user tasks are often specified in natural langua…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules
Yilun Liu, Yunpu Ma, Yuetian Lu +3
Mixture-of-Experts (MoE) benefits from a dynamic routing mechanism among their specialized experts, which existing Parameter- Efficient Fine-Tuning (PEFT) strategies fail to levera…
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Sikuan Yan, Xiufeng Yang, Zuchao Huang +10
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context win…
Supposedly Equivalent Facts That Aren't? Entity Frequency in Pre-training Induces Asymmetry in LLMs
Yuan He, Bailan He, Zifeng Ding +8
Understanding and mitigating hallucinations in Large Language Models (LLMs) is crucial for ensuring reliable content generation. While previous research has primarily focused on "w…
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
Yilun Liu, Yunpu Ma, Shuo Chen +4
The Mixture-of-Experts (MoE) paradigm has emerged as a powerful approach for scaling transformers with improved resource utilization. However, efficiently fine-tuning MoE models re…
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Zifeng Ding, Yifeng Li, Yuan He +5
Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temp…
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Shuo Chen, Zhen Han, Bailan He +5
Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some methods are not limited to the t…
Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial Image
Zefeng Wang, Zhen Han, Shuo Chen +6
Multimodal LLMs (MLLMs) with a great ability of text and image understanding have received great attention. To achieve better reasoning with MLLMs, Chain-of-Thought (CoT) reasoning…
Text2Loc: 3D Point Cloud Localization from Natural Language
Yan Xia, Letian Shi, Zifeng Ding +2
We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network, Text2Loc, that fully interprets the semant…
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
Zifeng Ding, Heling Cai, Jingpei Wu +4
Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embeddin…
Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs
Zifeng Ding, Jingcheng Wu, Jingpei Wu +2
Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide additional key-value pairs (i.e., qualifiers) for each KG fact that help to better restrict th…
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Zifeng Ding, Jingpei Wu, Zongyue Li +2
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predic…
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Zifeng Ding, Jingpei Wu, Bailan He +3
Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only de…
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Zifeng Ding, Zongyue Li, Ruoxia Qi +8
Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information f…
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Zifeng Ding, Bailan He, Yunpu Ma +2
Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly b…
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Zhen Han, Ruotong Liao, Jindong Gu +6
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embe…
A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
Zifeng Ding, Yunpu Ma, Bailan He +1
Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions…
Temporal Knowledge Graph Forecasting with Neural ODE
Zhen Han, Zifeng Ding, Yunpu Ma +2
There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches…