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Works113 from public data
- Qwen2.5 Technical Report5,408
The open-weight flagship Qwen2.5-72B-Instruct outperforms a number of open and proprietary models and demonstrates competitive performance to the state-of-the-art open-weight model, Llama-3-405B-Instruct, which is around 5 times larger.
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution5,204
The Qwen2-VL Series introduces the Naive Dynamic Resolution mechanism, which enables the model to dynamically process images of varying resolutions into different numbers of visual tokens, allowing the model to generate more efficient and accurate visual representations, closely aligning with human perceptual processes.
- Qwen Technical Report4,443
Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts, and includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques.
- Qwen3-VL Technical Report2,675
Qwen3-VL is introduced, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks, and three key upgrades are introduced, including an enhanced interleaved-MRoPE for stronger spatial-temporal modeling across images and video.
- Qwen2 Technical Report2,548
A comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to 72 billion, featuring dense models and a Mixture-of-Experts model, which surpasses most prior open-weight models and demonstrates robust multilingual capabilities.
- ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training512
A new sequence-to-sequence pre-training model called ProphetNet is presented, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism that predicts the next n tokens simultaneously based on previous context tokens at each time step.
- Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models155
This research significantly contributes to the detection of reliable answers generated by LLMs and holds noteworthy implications for mitigating hallucination in the future work.
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- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
- GLGE: A New General Language Generation Evaluation Benchmark75
The General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks, is presented and a leaderboard with strong baselines including MASS, BART, and ProphetNet is built.
- RikiNet: Reading Wikipedia Pages for Natural Question Answering59
This paper introduces a new model, called RikiNet, which reads Wikipedia pages for natural question answering, which is the first single model that outperforms the single human performance.
- Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation57
The proposed Tailor represents each attribute as a pre- trained continuous vector i.e., single-attribute prompt, which guides the generation of a fixed pre-trained language model (PLM) to satisfy aPre-specified attribute.
- Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning53
A new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer and significantly outperforms five state-of-the-art methods.
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- Interactive Natural Language Processing51
This paper provides a systematic classification of iNLP, dissecting its various components, including interactive objects, interaction interfaces, and interaction methods, and delves into the evaluation methodologies used in the field, exploring its diverse applications, and scrutinize its ethical and safety issues.
- μ-Forcing49
The proposed method directly injects extra constraints on the posteriors of latent variables into the learning process of VRAE, which can flexibly and stably control the tradeoff between the Kullback-Leibler (KL) term and the reconstruction term, making the model learn dense and meaningful latent representations.
- UniTE: Unified Translation Evaluation47
This paper proposes monotonic regional attention to control the interaction among input segments, and unified pretraining to better adapt multi-task training and can universally surpass various state-of-the-art or winner methods across tasks.
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- AnchiBERT: A Pre-Trained Model for Ancient Chinese Language Understanding and Generation42
AnchiBERT is released, a pre-trained language model based on the architecture of BERT, which is trained on large-scale ancient Chinese corpora and outperforms BERT as well as the non-pretrained models and achieves state-of-the-art results in all cases.
- Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space40
A novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension, question generation, and question-answering natural language inference tasks.
- Frequency-Aware Contrastive Learning for Neural Machine Translation36
A frequency-aware token-level contrastive learning method, in which the hidden state of each decoding step is pushed away from the counterparts of other target words in a soft contrastive way based on the corresponding word frequencies, which can not only significantly improve the translation quality but also enhance lexical diversity and optimize word representation space.
- Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation31
The proposed Tailor represents each attribute as a pre-trained continuous vector and guides the generation of a fixed PLM switch to aPre-specified attribute, which can be concatenated with any two single-attribute prompts to multi-attribute text generation.
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- A Multi-Modal Chinese Poetry Generation Model28
A three-stage multi-modal Chinese poetry generation approach which can effectively capture character, phrase, and sentence information between contexts and improve the symmetry delivered in poems is proposed.
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- GCPG: A General Framework for Controllable Paraphrase Generation23
The combination of lexical and syntactical conditions shows the significant controllable ability of paraphrase generation, and these empirical results could provide novel insight to user-oriented paraphrasing.
- Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation22
A multi-source Transformer decoder, which takes three sources as inputs and generates multiple headlines with keyphrases of user interests to generate multiple keyphrase-relevant headlines, achieves state-of-the-art results in terms of quality and diversity.
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- Ancient–Modern Chinese Translation with a New Large Training Dataset20
This article proposes an ancient–modern Chinese clause alignment approach based on the characteristics of these two languages that achieves 94.2 F1-score on the authors' manual annotation Test set and creates a new large-scale ancient– modern Chinese parallel corpus that contains 1.24M bilingual pairs.
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- POS-Constrained Parallel Decoding for Non-autoregressive Generation16
The proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding, and demonstrates that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation.
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- Deep Poetry: A Chinese Classical Poetry Generation System15
This work demonstrates a Chinese classical poetry generation system called Deep Poetry that uses neural networks that are trained on over 200 thousand poems and 3 million ancient Chinese prose to generate classical poetry.
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- Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints14
This work introduces controllable simile generation (CSG), a new task that requires the model to generate a simile with multiple simile elements, e.g., context and vehicle, and presents GraCe, a model that can serve as a strong baseline for CSG, especially with Scorer, which beats model-based retrieval methods without any re-training.
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- Revision in Continuous Space: Fine-Grained Control of Text Style Transfer.12
A new framework without adversarial learning is proposed, which consists of a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor and significantly outperforms five state-of-the-art methods.
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- An automatic evaluation metric for Ancient-Modern Chinese translation10
This paper proposed an automatic evaluation metric for Ancient-Modern Chinese Translation called DTE (Dual-based Translation Evaluation), which can be used to evaluate one-to-many alignment in the absence of multiple references and designed a new word segmentation method to improve the translation of proper nouns without increasing the size of the model vocabulary.
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- Generating Chinese Poetry from Images via Concrete and Abstract Information6
This paper proposes an infilling-based Chinese poetry generation model which can infill the Concrete keywords into each line of poems in an explicit way, and an abstract information embedding to integrate the Abstract information into generated poems.
- Unified Data Selection for LLM Reasoning5
High-Entropy Sum (HES) is established as a robust, training-free metric that enables a unified, effective, and efficient method for developing advanced reasoning in LLMs.
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- MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning4
This work evaluates MLLM's code-based capabilities in multi-modal mathematical reasoning and assesses the model's capacity for fine-grained operations, which include three types: Deletion, Modification and Annotation.
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- Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors4
This paper proposes Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs, and builds an FG-TED model, which can identify both error type and position concurrently.
- OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models4
An instruction-tuning dataset named OccuQuest is created, which contains 110,000+ prompt-completion pairs and 30,000- dialogues covering over 1,000 occupations in 26 occupational categories and significantly outperforms state-of-the-art LLaMA variants on professional questions in GPT-4 and human evaluations.
- CoupGAN: Chinese couplet generation via encoder–decoder model and adversarial training under global control4
This paper introduces a discriminative model to evaluate the global-level quality of generated sentences, named CoupGAN, and demonstrates that the proposed model is superior to existing methods in various evaluation metrics.
- An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model3
For downstream fine-tuning consistent with pre-training, data size no longer affects the performance, while the influence of fine-tunable parameter size is not monotonous, which could guide the choice of training strategy for various PEFTs.
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- Method to Improve the Performance of Restricted Boltzmann Machines2
A method to train RBM by adding reconstruction error to the cost function is put forward and the experimental results show that the proposed method can be effective.
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- Draft, Command, and Edit: Controllable Text Editing in E-Commerce–
A new draft-command-edit manner in description generation is explored, leading to the proposed new task—controllable text editing in E-commerce and a data augmentation method is designed to remedy the low resource challenge in this task.
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 2026-10-13. 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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