Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks32 from public data
- Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning96
This work designs a new backdoor attack method, named ICLAttack, to target large language models based on in-context learning, and encompasses two types of attacks: poisoning demonstration examples and poisoning demonstration prompts, which can make models behave in alignment with predefined intentions.
- 60
- Combining tag correlation and user social relation for microblog recommendation60
A joint framework to combine tag correlation and user social relation for microblog recommendation is investigated and an iterative updating scheme is developed to get the final tag-user matrix for computing the similarities between microblogs and users.
- Query Prior Matters53
A machine reading comprehension based framework for MNER, namely MRC-MNER, which outperforms the current state-of-the-art models on Twitter2017, and yields competitive results on Twitter2015.
- EvidenceNet: Evidence Fusion Network for Fact Verification34
The experimental results have shown that the proposed EvidenceNet model outperforms the current fact verification methods and achieves the state-of-the-art performance.
- Modularized Interaction Network for Named Entity Recognition32
A novel Modularized Interaction Network (MIN) model is proposed which utilizes both segment-level information and word-level dependencies, and incorporates an interaction mechanism to support information sharing between boundary detection and type prediction to enhance the performance for the NER task.
- E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service23
E-ConvRec is constructed, an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base (KB), and multiple sequential real conversations between users and recommenders to explore conversational recommendation in a real scene from multiple facets based on the dataset.
- 20
- 20
- A Microblog Recommendation Algorithm Based on Multi-tag Correlation11
A microblog recommendation algorithm based on multi-tag correlation that combines inner and outer correlation between tags to conquer the problem of sparsity of user-tag matrix is presented.
- 9
- 8
- A syntactic evidence network model for fact verification6
The proposed SENet model has outperformed the baseline models and achieved the state-of-the-art performance for fact verification and the sentence attention mechanism is applied to obtain a richer semantic representation.
- 6
- Latent relation shared learning for endometrial cancer diagnosis with incomplete multi-modality medical images4
The experimental results show that the proposed incomplete multi-modality images data fusion method outperforms the current representative approaches in terms of classification accuracy, sensitivity, specificity, and area under curve (AUC).
- A syntactic multi-level interaction network for rumor detection4
A syntactic multi-level interaction network model which incorporates syntactic dependency relationships and multi-level interaction network for rumor detection and incorporates the extracted syntactic dependency relationships into the attention mechanism for language-driven word representation is proposed.
- 4
- Semi-supervised Nonnegative Matrix Factorization for Microblog Clustering Based on Term Correlation4
A semi-supervised Nonnegative Matrix Factorization clustering method based on term correlation data is proposed, which well captures the semantic information for term weighting in microblogs.
- 3
- 3
- A term correlation based semi-supervised microblog clustering with dual constraints3
A novel semi-supervised learning scheme fully exploring the semantic information to compensate for the limited message length of microblogs, which greatly reduces the labor-intensive labeling process, but also deeply exploits hidden information from microblog itself.
- An Efficient Microblog Hot Topic Detection Algorithm Based on Two Stage Clustering3
A two stage clustering algorithm based on probabilistic latent semantic analysis (pLSA) and K-means clustering (K-me means) is proposed and experiments show that this method can effectively cluster topics and be applied to microblog hot topic detection.
- Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction2
This paper designs a novel separation-and-fusion paradigm to separately acquire cross-event information and fuse it into the argument extraction of a target event, and proposes a novel multiple token linking model named Sep2F, which outperforms the state-of-the-art EAE models on all the datasets.
- 2
- 2
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.