36 citations · 187 across the 42 of their papers we have counts for
5 papers · 1 filter
CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarization
Yang Zhao, Di Huang, Chongxiao Li +14
The design flow of processors, particularly in hardware description languages (HDL) like Verilog and Chisel, is complex and costly. While recent advances in large language models (…
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Yutong Wu, Di Huang, Wenxuan Shi +13
Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to…
Prompt-based Visual Alignment for Zero-shot Policy Transfer
Haihan Gao, Rui Zhang, Qi Yi +13
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature ex…
Luban: Building Open-Ended Creative Agents via Autonomous Embodied Verification
Yuxuan Guo, Shaohui Peng, Jiaming Guo +15
Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined…
Assessing and Understanding Creativity in Large Language Models
Yunpu Zhao, Rui Zhang, Wenyi Li +10
In the field of natural language processing, the rapid development of large language model (LLM) has attracted more and more attention. LLMs have shown a high level of creativity i…