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Works108 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.
- 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.
- Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond1,965
The Qwen-VL series is introduced, a set of large-scale vision-language models designed to perceive and understand both text and images that outperforms existing Large Vision Language Models (LVLMs).
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models1,618
The Qwen3 Embedding series is introduced, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models, and achieves state-of-the-art results across diverse benchmarks.
- Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement1,174
A series of math-specific large language models that possess advanced mathematical reasoning capabilities, including Chain-of-Thought (CoT) and Tool-Integrated Reasoning (TIR), and are evaluated on 10 mathematics datasets in both English and Chinese.
- Qwen3-Omni Technical Report582
Qwen3-Omni is presented, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relative to single-modal counterparts and introduces a Thinking model that explicitly reasons over inputs from any modality.
- Towards Knowledge-Based Recommender Dialog System318
A novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System, integrates the recommender system and the dialog generation system and shows that the two systems can bring mutual benefits to each other.
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- ExpertPrompting: Instructing Large Language Models to be Distinguished Experts235
This paper proposes ExpertPrompting to elicit the potential of LLMs to answer as distinguished experts, and produces a competitive open-source chat assistant called ExpertLLaMA, which outperforms existing open-source opponents and achieves 96\% of the original ChatGPT's capability.
- Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese229
This work constructs a large-scale dataset of image-text pairs in Chinese, where most data is retrieved from publicly available datasets, and proposes a two-stage pretraining method, where the model is first trained with the image encoder frozen and then trained with all parameters being optimized to achieve enhanced model performance.
- Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)220
It is proved that for the multi-modal late-fusion network with (smoothed) ReLU activation trained jointly by gradient descent, different modalities will compete with each other, and the encoder networks will learn only a subset of modalities.
- Qwen2.5-1M Technical Report201
This framework provides an efficient and powerful solution for developing applications that require long-context processing using open-source models and detail the optimizations in the inference engine, including kernel optimization, pipeline parallelism, and scheduling optimization, which significantly enhance overall inference performance.
- ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities176
This work releases ONE-PEACE, a highly extensible model with 4B parameters that can seamlessly align and integrate representations across vision, audio, and language modalities, and develops two modality-agnostic pretraining tasks, which align the semantic space of different modalities and capture fine-grained details within modalities concurrently.
- Qwen3-ASR Technical Report175
Timestamp accuracy experiments show that the proposed model outperforms the three strongest force alignment models and takes more advantages in efficiency and versatility, and is released under the Apache 2.0 license.
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- Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation154
A novel language-model based discriminator is proposed, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators.
- Global Encoding for Abstractive Summarization153
A global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context, and consists of a convolutional gated unit to perform global encoding to improve the representations of thesource-side information.
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- #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models135
This work proposes InsTag, an open-set fine-grained tagger, to tag samples within SFT datasets based on semantics and intentions and defines instruction diversity and complexity regarding tags, and obtains 6.6K tags to describe comprehensive user queries.
- CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings120
This work introduces CodeElo, a standardized competition-level code generation benchmark that effectively addresses all these challenges for the first time and provides the Elo ratings of 30 existing popular open-source and 3 proprietary LLMs for the first time.
- CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy103
CC-OCR aims to comprehensively evaluate the capabilities of LMMs on OCR-centered tasks, facilitating continued progress in literacy with rich structure and fine-grained visual challenges.
- Imitation Learning for Non-Autoregressive Neural Machine Translation99
This paper proposes an imitation learning framework for non-autoregressive machine translation, which still enjoys the fast translation speed but gives comparable translation performance compared to its auto-regressive counterpart.
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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