24 citations · 70 across the 46 of their papers we have counts for
15 papers · 2 filters
M2G-Eval: Enhancing and Evaluating Multi-granularity Multilingual Code Generation
Fanglin Xu, Wei Zhang, Jian Yang +5
The rapid advancement of code large language models (LLMs) has sparked significant research interest in systematically evaluating their code generation capabilities, yet existing b…
CodeSimpleQA: Scaling Factuality in Code Large Language Models
Jian Yang, Wei Zhang, Yizhi Li +8
Large language models (LLMs) have made significant strides in code generation, achieving impressive capabilities in synthesizing code snippets from natural language instructions. H…
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models
Jiajun Wu, Jian Yang, Wei Zhang +6
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive l…
Scaling Laws for Code: Every Programming Language Matters
Jian Yang, Shawn Guo, Lin Jing +8
Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming…
M3TQA: Massively Multilingual Multitask Table Question Answering
Daixin Shu, Jian Yang, Zhenhe Wu +11
Tabular data is a fundamental component of real-world information systems, yet most research in table understanding remains confined to English, leaving multilingual comprehension…
IFEvalCode: Controlled Code Generation
Jian Yang, Wei Zhang, Shukai Liu +9
Code large language models (Code LLMs) have made significant progress in code generation by translating natural language descriptions into functional code; however, real-world appl…
Multilingual Multimodal Software Developer for Code Generation
Linzheng Chai, Jian Yang, Shukai Liu +12
The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting crucial visual aids like diagrams and…
A Survey on Latent Reasoning
Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng +30
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate s…
P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark
Tao Sun, Enhao Pan, Zhengkai Yang +8
Academic posters are vital for scholarly communication, yet their manual creation is time-consuming. However, automated academic poster generation faces significant challenges in p…
KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
Jiajun Shi, Jian Yang, Jiaheng Liu +26
Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchm…
Table-R1: Region-based Reinforcement Learning for Table Understanding
Zhenhe Wu, Jian Yang, Zhongjiang He +9
Tables present unique challenges for language models due to their structured row-column interactions, necessitating specialized approaches for effective comprehension. While large…
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
P Team, Xinrun Du, Yifan Yao +94
Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…
A Comprehensive Survey on Long Context Language Modeling
Jiaheng Liu, Dawei Zhu, Zhiqi Bai +34
Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it…
Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow
Jiaqi Bai, Hongcheng Guo, Zhongyuan Peng +4
Large vision-language models show tremendous potential in understanding visual information through human languages. However, they are prone to suffer from object hallucination, i.e…
SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
Xianfu Cheng, Wei Zhang, Shiwei Zhang +16
The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly t…