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cs.CL2023
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models
Zhou Ziheng, Yingnian Wu, Song-Chun Zhu +1
We introduce Aligner, a novel Parameter-Efficient Fine-Tuning (PEFT) method for aligning multi-billion-parameter-sized Large Language Models (LLMs). Aligner employs a unique design…
cs.AI2023★ 2 cited
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
Ran Gong, Jiangyong Huang, Yizhou Zhao +9
Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g.,…