370 citations · 522 across the 44 of their papers we have counts for
7 papers · 1 filter
EntSQL: A Benchmark for Grounding Text-to-SQL in Long-Context Enterprise Knowledge
Chengxi Liao, Tao Xu, Zulong Chen +7
Text-to-SQL enables natural language access to databases, and recent LLMs have substantially advanced its capabilities. Existing benchmarks such as Spider, BIRD, and Spider~2.0 eva…
Multi-domain Multi-modal Document Classification Benchmark with a Multi-level Taxonomy
Denghao Ma, Qing Liu, Zulong Chen +5
Document classification forms the backbone of modern enterprise content management, yet existing benchmarks remain trapped in oversimplified paradigms -- single domain settings wit…
CC-OCR V2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding
Chunyi Peng, Zhipeng Xu, Yuqi Xiong +12
Recent Large Multimodal Models (LMMs) have achieved remarkable progress on OCR-centric document understanding and processing tasks. Existing benchmarks primarily evaluate LMMs acro…
Triviality Corrected Endogenous Reward
Xinda Wang, Zhengxu Hou, Yangshijie Zhang +6
Reinforcement learning for open-ended text generation is constrained by the lack of verifiable rewards, necessitating reliance on judge models that require either annotated data or…
Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
Mingxin Li, Yanzhao Zhang, Dingkun Long +9
In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, t…
Layout-Aware Parsing Meets Efficient LLMs: A Unified, Scalable Framework for Resume Information Extraction and Evaluation
Fanwei Zhu, Jinke Yu, Zulong Chen +6
Automated resume information extraction is critical for scaling talent acquisition, yet its real-world deployment faces three major challenges: the extreme heterogeneity of resume…
EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation
Xinda Wang, Zhengxu Hou, Yangshijie Zhang +6
Although the effectiveness of Large Language Models (LLMs) as judges (LLM-as-a-judge) has been validated, their performance remains limited in open-ended tasks, particularly in sto…