Parameter-efficient fine-tuning for large models: A comprehensive survey
Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …
enabling remarkable achievements across various tasks. However, their unprecedented …
Retrieval augmented generation (rag) and beyond: A comprehensive survey on how to make your llms use external data more wisely
Large language models (LLMs) augmented with external data have demonstrated
remarkable capabilities in completing real-world tasks. Techniques for integrating external …
remarkable capabilities in completing real-world tasks. Techniques for integrating external …
Survey of different large language model architectures: Trends, benchmarks, and challenges
Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …
understanding natural language and generating coherent responses to various prompts or …
Alora: Allocating low-rank adaptation for fine-tuning large language models
Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in
the era of large language models. Low-rank adaptation (LoRA) has demonstrated …
the era of large language models. Low-rank adaptation (LoRA) has demonstrated …
Milora: Efficient mixture of low-rank adaptation for large language models fine-tuning
Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective
parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latency …
parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latency …
IAPT: Instance-Aware Prompt Tuning for Large Language Models
W Zhu, A Tian, C Yin, Y Ni, X Wang… - Proceedings of the 62nd …, 2024 - aclanthology.org
Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it
has a clear drawback: many soft tokens must be inserted into the input sequences to …
has a clear drawback: many soft tokens must be inserted into the input sequences to …
StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
Large language models have shown their ability to become effective few-shot learners with
prompting, revoluting the paradigm of learning with data scarcity. However, this approach …
prompting, revoluting the paradigm of learning with data scarcity. However, this approach …
Overview of the promptCBLUE shared task in CHIP2023
This paper presents an overview of the PromptCBLUE shared task (http://cips-chip. org.
cn/2023/eval1) held in the CHIP-2023 Conference. This shared task reformulates the …
cn/2023/eval1) held in the CHIP-2023 Conference. This shared task reformulates the …
Parameter-efficient fine-tuning in large models: A survey of methodologies
L Wang, S Chen, L Jiang, S Pan, R Cai, S Yang… - arxiv preprint arxiv …, 2024 - arxiv.org
The large models, as predicted by scaling raw forecasts, have made groundbreaking
progress in many fields, particularly in natural language generation tasks, where they have …
progress in many fields, particularly in natural language generation tasks, where they have …
Text2MDT: extracting medical decision trees from medical texts
Knowledge of the medical decision process, which can be modeled as medical decision
trees (MDTs), is critical to build clinical decision support systems. However, the current MDT …
trees (MDTs), is critical to build clinical decision support systems. However, the current MDT …