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Works83 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.
- 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.
- 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.
- BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions724
An extensive evaluation of 60 LLMs shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%, which underscores the need for further advancements in this area.
- OLMoE: Open Mixture-of-Experts Language Models352
This work introduces OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE), and presents various experiments on MoE training, analyzes routing in the model showing high specialization, and open-source all aspects of the work: model weights, training data, code, and logs.
- Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning288
This work exploits large language models (LLMs) as decomposers for effective table-based reasoning, and proposes a novel ''parsing-execution-filling'' strategy to decompose a complex question into simper step-by-step sub-questions by generating intermediate SQL queries as a bridge.
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- 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.
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- A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions110
A comprehensive review on deep learning approaches for text-to-SQL parsing, including a systematical overview of pre-trained language models and existing methods, is presented.
- Lemur: Harmonizing Natural Language and Code for Language Agents98
Lemur and Lemur-Chat are introduced, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either.
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- Qwen3-Coder-Next Technical Report83
This work explores how far strong training recipes can push the capability limits of models with small parameter footprints and presents Qwen3-Coder-Next, an open-weight language model specialized for coding agents that activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference.
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- Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning76
This paper proposes a multi-task graph learning model, called collaborative graph convolutional networks (CGCN), composed of an attributed graph clustering network and a semi-supervised node classification network, designed by combining variational graph auto-encoder with Gaussian mixture models (GMM-VGAE).
- PolyLM: An Open Source Polyglot Large Language Model75
PolyLM, a multilingual LLM trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B, surpasses other open-source models such as LLaMA and BLOOM on multilingual tasks while maintaining comparable performance in English.
- Latent Heterogeneous Graph Network for Incomplete Multi-View Learning69
A novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning is proposed, which aims to use multiple incomplete views as fully as possible in a flexible manner and explores the complex relationship between samples and latent representations.
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- Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing56
A novel framework to elicit relational structures from large-scale pre-trained language models (PLMs) via a probing procedure based on Poincaré distance metric, and use the induced relations to augment current graph-based parsers for better schema linking is proposed.
- Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation52
It is shown that state-of-the-art LLMs struggle to solve ProverQA problems, even with CoT prompting, highlighting the dataset's challenging nature, and the value of the proposed data generation framework.
- Dynamic Hybrid Relation Exploration Network for Cross-Domain Context-Dependent Semantic Parsing52
This paper presents a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds, and employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation.
- Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study45
DevBench is proposed, a comprehensive benchmark that evaluates LLMs across various stages of the software development lifecycle, including software design, environment setup, implementation, acceptance testing, and unit testing.
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 2026-10-14. 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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