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- ArnetMiner2,609
The architecture and main features of the ArnetMiner system, which aims at extracting and mining academic social networks, are described and a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues is proposed.
- ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools1,810
This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B, which represent the most capable models that are trained with all the insights and lessons gained from the preceding three generations of ChatGLM.
- Parameter-efficient fine-tuning of large-scale pre-trained language models1,311
Different methods of fine-tuning with only a small number of parameters are compared on a large set of natural language processing tasks and demonstrates that large-scale models could be effectively stimulated by the optimization of a few parameters.
- KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation883
A unified model for Knowledge Embedding and Pre-trained LanguagERepresentation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs is proposed.
- CogVLM: Visual Expert for Pretrained Language Models864
This work introduces CogVLM, a powerful open-source visual language foundation model that bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers, enabling deep fusion of vision language features without sacrificing any performance on NLP tasks.
- GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models474
GLM-4.5 is presented, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes that ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks.
- 474
- Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification456
This work focuses on incorporating external knowledge into the verbalizer, forming a knowledgeable prompt Tuning (KPT), to improve and stabilize prompttuning.
- OpenKE: An Open Toolkit for Knowledge Embedding376
An open toolkit for knowledge embedding, which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space and the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are available.
- Multi-Channel Graph Neural Network for Entity Alignment309
A novel Multi-channel Graph Neural Network model (MuGNN) is proposed to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels and infer and transfer rule knowledge for completing two KG consistently.
- Understanding retweeting behaviors in social networks273
This paper proposes a factor graph model to predict users' retweeting behaviors and shows that this method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.
- CogVLM2: Visual Language Models for Image and Video Understanding267
The CogVLM2 family, a new generation of visual language models for image and video understanding including CogVLM2, CogVLM2-Video and GLM-4V are proposed, achieving state-of-the-art results on benchmarks like MMBench, MM-Vet, TextVQA, MVBench and VCGBench.
- Explainable and Explicit Visual Reasoning Over Scene Graphs265
The prevalent black-box neural architectures used in complex visual reasoning tasks are dismantled into the proposed eXplainable and eXplicit Neural Modules (XNMs), which advance beyond existing neural module networks towards using scene graphs --- objects as nodes and the pairwise relationships as edges --- for explainable and explicit reasoning with structured knowledge.
- Expert Finding in a Social Network255
Experimental results show that the proposed propagation-based approach can outperform the baseline approach and take into consideration both person local information and network information (e.g. relationships between persons).
- A Survey of Knowledge Enhanced Pre-Trained Language Models251
A comprehensive review of Knowledge Enhanced Pre-trained Language Models (KE-PLMs) is presented to provide a clear insight into this thriving field and introduces appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight these two main tasks of NLP.
- Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models250
The theoretical principles underlying the effectiveness of delta tuning are discussed and frameworks to interpret delta tuning from the perspective of optimization and optimal control are proposed, where results on over 100 NLP tasks demonstrate a comprehensive performance comparison of different approaches.
- Benchmarking Foundation Models with Language-Model-as-an-Examiner249
A novel benchmarking framework where the LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner is proposed, allowing for effortless extensibility as various LMs can be adopted as the examiner and the questions can be constantly updated given more diverse trigger topics.
- MAVEN: A Massive General Domain Event Detection Dataset232
A MAssive eVENt detection dataset is presented, which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types and shows that existing ED methods cannot achieve promising results as on the small datasets, which suggests ED in real world remains a challenging task and requires further research efforts.
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- Text-enhanced representation learning for knowledge graph216
The rich textual context information in a text corpus is incorporated to expand the semantic structure of the knowledge graph and each relation is enabled to own different representations for different head and tail entities to better handle 1-to-N, N- to-1 and N-To-N relations.
- Keyword Extraction Using Support Vector Machine211
Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction and the accuracy of document classification can be significantly improved by using the keyword extraction method.
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- Who Influenced You? Predicting Retweet via Social Influence Locality200
A novel notion of social influence locality is proposed and two instantiation functions based on pairwise influence and structural diversity are developed, which can further leverage the network-based correlation.
- RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style192
RM-Bench is introduced, a novel benchmark designed to evaluate reward models based on their sensitivity to subtle content differences and resistance to style biases, and it is revealed that even state-of-the-art models achieve an average performance, which falls short of random-level accuracy when faced with style bias interference.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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