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- Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing188
An in-depth probing analysis is conducted and it is demonstrated that cross-attention maps in Stable Diffusion often contain object attribution information, which can result in editing failures, and proposes a simplified, yet more stable and efficient, tuning-free procedure that modifies only the self-attention maps of specified attention layers during the denoising process.
- A retrospective of knowledge graphs158
This paper discusses in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning.
- Development and evaluation of a Web 2.0 annotation system as a learning tool in an e-learning environment140
The evaluation results showed that, with factor analysis, interactivity, usefulness, helpfulness, and willingness for future use were categorized to represent the perceptions of MyNote and the factors of interactivity and helpfulness were statistically significant to predict the future use of My note.
- Video Generation From Single Semantic Label Map111
A cVAE for predicting optical flow is employed as a beneficial intermediate step to generate a video sequence conditioned on the initial single frame, and a semantic label map is integrated into the flow prediction module to achieve major improvements in the image-to-video generation process.
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- A Customized Text Sanitization Mechanism with Differential Privacy93
A novel Customized Text (CusText) sanitization mechanism based on the original $\epsilon$-differential privacy (DP) definition, which is compatible with any similarity measure, and achieves a better trade-off between privacy and utility than existing mechanisms.
- A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances71
Recent advances on taxonomy construction from free texts are overviewed, reorganizing relevant subtasks into a complete framework for evaluation and discussing challenges for future research.
- Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains70
This work proposes a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students and demonstrates the effectiveness and superiority of this framework on public multi-domain NLP tasks.
- TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification66
In TransPrompt, a transferable prompting framework for few-shot learning across similar NLP tasks, a multi-task meta-knowledge acquisition procedure is employed to train a meta-learner that captures cross-task transferable knowledge.
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- SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining61
In SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure.
- Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning44
CP-Tuning is presented, an end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers, and is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters.
- HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction44
A three-level hierarchical learning framework to interact with cross levels is proposed, generating the de-noising context-aware representations via adapting the existing multi-head self-attention, named Multi-Granularity Recontextualization.
- DKPLM: Decomposable Knowledge-Enhanced Pre-trained Language Model for Natural Language Understanding44
A novel KEPLM named DKPLM is proposed that decomposes knowledge injection process of the pre-trained language models in pre-training, fine-tuning and inference stages, which facilitates the applications of KEPLMs in real-world scenarios.
- Improving Clinical Named Entity Recognition with Global Neural Attention40
The authors' model outperforms previous state-of-the-art method with less labeled data and no feature engineering, and is evaluated on 2010 i2b2/VA datasets to verify the effectiveness of leveraging global information and transfer strategy.
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- From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression34
ContrAstive Pruning (CAP) is proposed, designed as a general framework, compatible with both structured and unstructured pruning, and enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model fortask-specific knowledge.
- MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning34
The Meta Lifelong Learning (MeLL) framework is introduced, a BERT-based text encoder is employed to learn robust text representations across tasks, which is slowly updated for lifelong learning and improves the performance compared with strong baselines and also reduces the number of total parameters.
- DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding33
This paper introduces DocLayLLM, an efficient multi-modal extension of LLMs specifically designed for TDU, and deeply considered the role of chain-of-thought (CoT) and innovatively proposed the techniques of CoT Pre-training and CoT Annealing.
- MCL: Multi-Granularity Contrastive Learning Framework for Chinese NER28
This paper proposes a novel Multi-Granularity Contrastive Learning framework (MCL), that aims to optimize the inter-granularity distribution distance and emphasize the critical matched words in the lexicon and obtains state-of-the-art results while considering model efficiency.
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- Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining24
An effective learning procedure named Meta Fine-Tuning (MFT), served as a meta-learner to solve a group of similar NLP tasks for neural language models, and is implemented upon BERT to solve several multi-domain text mining tasks.
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 2026-10-16. 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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