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- Shouxun LinSuggested from co-authorship
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Works323 from public data
- 491
- Large Language Model Alignment: A Survey338
This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs, in conjunction with the extant capability research in this domain, and envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.
- Evaluating Large Language Models: A Comprehensive Survey328
This survey endeavors to offer a panoramic perspective on the evaluation of LLMs, and categorizes the evaluated into three major groups: knowledge and capability evaluation, alignment evaluation and safety evaluation, which will channel their evolution into a direction that maximizes societal benefit while minimizing potential risks.
- Maximum entropy based phrase reordering model for statistical machine translation270
A novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs) that obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.
- Variational Neural Machine Translation211
This paper builds a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound, and shows that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machinetranslation baselines.
- 173
- Modeling Source Syntax for Neural Machine Translation150
This paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements, and linearize parse trees of source sentences to obtain structural label sequences.
- Semantic Similarity from Natural Language and Ontology Analysis135
This book proposes an in-depth characterization of existing proposals for semantic similarity estimation by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications, and provides a detailed discussion on the foundations of semantic measures.
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- Accelerating Neural Transformer via an Average Attention Network129
The proposed average attention network is applied on the decoder part of the neural Transformer to replace the original target-side self-attention model and enables the neuralTransformer to decode sentences over four times faster than its original version with almost no loss in training time and translation performance.
- Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition116
A Shallow Convolutional Neural Network (SCNN) is proposed, which contains only one hidden layer but is effective in relation recognition, which alleviates the overfitting problem, while the convolution and nonlinear operations help preserve the recognition and generalization ability of the model.
- Neural Machine Translation Advised by Statistical Machine Translation96
Experimental results on Chinese-English translation show that the proposed approach achieves significant and consistent improvements over state-of-the-art NMT and SMT systems on multiple NIST test sets.
- 92
- Variational Recurrent Neural Machine Translation84
A novel variational recurrent neural machine translation model that introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable.
- 82
- Large Language Model Safety: A Holistic Survey81
This survey provides a comprehensive overview of the current landscape of LLM safety, covering four major categories: value misalignment, robustness to adversarial attacks, misuse, and autonomous AI risks.
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- Parsing the Penn Chinese Treebank with Semantic Knowledge70
Analysis of performance improvement indicates that semantic knowledge is helpful for nominal compounds, coordination, and N⋄V tagging disambiguation, as well as alleviating the sparseness of information available in treebank.
- Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation68
This paper proposes a hierarchical model to learn the global context for document-level neural machine translation (NMT), which feedback the extracted global document context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.
- Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning66
This work proposes a progressive self-supervised attention learning approach for attentional ABSA models that enhances the performance of all three models and integrates the proposed approach into three state-of-the-art neuralABSA models.
- Modeling the Translation of Predicate-Argument Structure for SMT66
Two discriminative, feature-based models to exploit predicate-argument structures for statistical machine translation are proposed: 1) a predicate translation model and 2) an argument reordering model.
- RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling64
RiSAWOZ is a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations, which contains 11.2K human-to-human multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets.
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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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