Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?

3 October 2023

Papers citing "Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?"

9 / 9 papers shown

Title
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey) S. Oota Zijiao Chen Manish Gupta R. Bapi G. Jobard F. Alexandre X. Hinaut 3DV AI4CE 49 11 0 31 Dec 2024
Prompt Your Brain: Scaffold Prompt Tuning for Efficient Adaptation of fMRI Pre-trained Model Zijian Dong Yilei Wu Zijiao Chen Yichi Zhang Yueming Jin Juan Helen Zhou 36 1 0 20 Aug 2024
Divergences between Language Models and Human Brains Yuchen Zhou Emmy Liu Graham Neubig Michael J. Tarr Leila Wehbe 35 1 0 15 Nov 2023
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 328 4,077 0 24 May 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 333 11,953 0 04 Mar 2022
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects Charlotte Caucheteux Alexandre Gramfort J. King 42 32 0 12 Oct 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,858 0 18 Apr 2021
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 243 1,452 0 18 Mar 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 297 6,959 0 20 Apr 2018