A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Chenhui Gou, Haoqin Tu, Yunhao Fang, et al.
Experience Distillation internalizes in-context learning gains from agent interaction histories into model weights without additional environment samples, retaining over 64% of ICL benefits.
Hao Liang, Qihan Lin, Zhaoyang Han, et al.
Introduces K12-KGraph, a curriculum-aligned knowledge graph from Chinese textbooks, with benchmark and training data to improve LLMs' curriculum cognition.
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Gora introduces gradient-driven adaptive low-rank adaptation that matches full fine-tuning performance.
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This paper analyzes the expressive power of Low-Rank Adaptation (LoRA) for fine-tuning pre-trained models.
Baohao Liao, Yan Meng, C. Monz
This paper proposes a parameter-efficient fine-tuning method that matches full fine-tuning performance without introducing additional inference latency.
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FourierFT is a parameter-efficient fine-tuning method that learns weight updates in the frequency domain via discrete Fourier transform.
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A survey and benchmark of parameter-efficient fine-tuning methods for pre-trained vision models.
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This survey provides a comprehensive overview of parameter-efficient fine-tuning (PEFT) methodologies for large language models, addressing the computational challenges of full fine-tuning.
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Parameter-efficient fine-tuning (PEFT) achieves better performance and lower cost than in-context learning for few-shot tasks.
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This paper critically reviews and assesses parameter-efficient fine-tuning methods for pretrained language models, highlighting their ability to reduce parameters and memory while maintaining performance.
Ning Ding, Yujia Qin, Guang Yang, et al.
This paper surveys parameter-efficient fine-tuning methods for large pre-trained language models, categorizing approaches and analyzing their trade-offs.
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A systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers from 2019 to 2024.