7 citations · 22 across the 7 of their papers we have counts for
7 papers
Generating Symbolic World Models via Test-time Scaling of Large Language Models
Zhouliang Yu, Yuhuan Yuan, Tim Z. Xiao +5
Solving complex planning problems requires Large Language Models (LLMs) to explicitly model the state transition to avoid rule violations, comply with constraints, and ensure optim…
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
Siming Huang, Tianhao Cheng, J. K. Liu +16
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are in…
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
Xinrun Du, Zhouliang Yu, Songyang Gao +11
In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs. Uniquely initiate…
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Jiawei Guo, Ziming Li, Xueling Liu +13
Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to r…
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
Chen Yang, Junzhuo Li, Xinyao Niu +11
Uncovering early-stage metrics that reflect final model performance is one core principle for large-scale pretraining. The existing scaling law demonstrates the power-law correlati…
Massive Editing for Large Language Models via Meta Learning
Chenmien Tan, Ge Zhang, Jie Fu
While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which…
AutoAgents: A Framework for Automatic Agent Generation
Guangyao Chen, Siwei Dong, Yu Shu +5
Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on pr…