语言大模型 / LLM

RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models

Saeed Najafi, Alona Fyshe

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Comments Final Version (Findings of ACL2024)

FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning

Yuwei Fu, Haichao Zhang, Di Wu, Wei Xu, Benoit Boulet

专题命中 指令微调 ：language model(title,abstract)；分类 cs.AI、cs.LG

Comments ICML 2024

Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning

Shen Li, Liuyi Yao, Jinyang Gao, Lan Zhang, Yaliang Li

专题命中 指令微调 ：LLM(title,abstract)；分类 cs.AI、cs.LG

Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models

Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan

专题命中 指令微调 ：language model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted by ICML 2024

LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models

Qin Yang, Meisam Mohammad, Han Wang, Ali Payani, Ashish Kundu, Kai Shu, Yan Yan, Yuan Hong

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Comments 18 pages, 15 figures

Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)

Tong Zhan, Chenxi Shi, Yadong Shi, Huixiang Li, Yiyu Lin

专题命中 指令微调 ：LLM(title)；language model(abstract)；分类 cs.CL、cs.AI

Revisiting the Role of Language Priors in Vision-Language Models

Zhiqiu Lin, Xinyue Chen, Deepak Pathak, Pengchuan Zhang, Deva Ramanan

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.AI

Comments Published at ICML 2024. Website: https://linzhiqiu.github.io/papers/visual_gpt_score/

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

Zhiqiang Tang, Haoyang Fang, Su Zhou, Taojiannan Yang, Zihan Zhong, Tony Hu, Katrin Kirchhoff, George Karypis

专题命中 指令微调 ：foundation model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted at AutoML 2024 Conference

Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition

Xiao Wang, Qian Zhu, Jiandong Jin, Jun Zhu, Futian Wang, Bo Jiang, Yaowei Wang, Yonghong Tian

专题命中 指令微调 ：foundation model(title,abstract)；分类 cs.CL、cs.AI

Comments Parameter Efficient Fine-Tuning Strategy for Video-based Pedestrian Attribute Recognition

FedPFT: Federated Proxy Fine-Tuning of Foundation Models

Zhaopeng Peng, Xiaoliang Fan, Yufan Chen, Zheng Wang, Shirui Pan, Chenglu Wen, Ruisheng Zhang, Cheng Wang

专题命中 指令微调 ：foundation model(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted by IJCAI'24

Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models

Yang Tan, Mingchen Li, Bingxin Zhou, Bozitao Zhong, Lirong Zheng, Pan Tan, Ziyi Zhou, Huiqun Yu, Guisheng Fan, Liang Hong

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Comments 30 pages, 4 figures, 8 tables

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

Jingyuan Selena She, Christopher Potts, Samuel R. Bowman, Atticus Geiger

专题命中 指令微调 ：language model(title)；pretraining(abstract)；分类 cs.CL、cs.LG

IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators

Indraneil Paul, Goran Glavaš, Iryna Gurevych

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.AI

When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations

Aleksandar Petrov, Philip H. S. Torr, Adel Bibi

专题命中 指令微调 ：prompting(title,abstract)；分类 cs.CL、cs.LG

Comments Accepted at ICLR 2024

Decision Transformer as a Foundation Model for Partially Observable Continuous Control

Xiangyuan Zhang, Weichao Mao, Haoran Qiu, Tamer Başar

专题命中 指令微调 ：foundation model(title)；language model(abstract)；分类 cs.AI、cs.LG

Comments Submitted to CDC 2024

LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction

Bo Zou, Chao Yang, Yu Qiao, Chengbin Quan, Youjian Zhao

专题命中 指令微调 ：instruction tuning(title,abstract)；分类 cs.CL、cs.AI

Comments This paper is accepted by CVPR 2024

Fine-Tuning Language Models Using Formal Methods Feedback

Yunhao Yang, Neel P. Bhatt, Tyler Ingebrand, William Ward, Steven Carr, Zhangyang Wang, Ufuk Topcu

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.AI

Fine-Tuning Pre-trained Language Models to Detect In-Game Trash Talks

Daniel Fesalbon, Arvin De La Cruz, Marvin Mallari, Nelson Rodelas

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Journal ref IJFMR Volume 6, Issue 2, March-April 2024

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, Lijuan Wang

专题命中 指令微调 ：instruction tuning(title,abstract)；分类 cs.CL、cs.AI

Comments 40 pages, 32 figures, ICLR 2024

Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization

Shuo Yang, Gjergji Kasneci

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.AI

Comments 12 pages, 2 figures

Tuning-less Object Naming with a Foundation Model

Andrej Lucny, Pavel Petrovic

专题命中 指令微调 ：foundation model(title,abstract)；分类 cs.CL、cs.AI

Comments This work was funded (or co-funded) by the Horizon-Widera-2021 European Twinning project TERAIS G.A. n. 101079338 World Symposium on Digital Intelligence for Systems and Machines (DISA2023), Kosice, September 21-22, 2023 citations: https://ieeexplore.ieee.org/document/10308905 codes: https://github.com/andylucny/whatisthis, https://doi.org/10.5281/zenodo.10702868 7 pages, 9 figures, 0 tables

Journal ref 2023 World Symposium on Digital Intelligence for Systems and Machines (DISA) https://ieeexplore.ieee.org/xpl/conhome/10308901/proceeding pages 154-160

Linguistic Profiling of a Neural Language Model

Alessio Miaschi, Dominique Brunato, Felice Dell'Orletta, Giulia Venturi

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted to COLING 2020

Journal ref Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020)

Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews

Ali Boluki, Javad Pourmostafa Roshan Sharami, Dimitar Shterionov

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Journal ref Proceedings of the 2023 Intelligent Systems Conference (IntelliSys), vol. 4, (2023), 15-35

Prompting a Pretrained Transformer Can Be a Universal Approximator

Aleksandar Petrov, Philip H. S. Torr, Adel Bibi

专题命中 指令微调 ：prompting(title,abstract)；分类 cs.AI、cs.LG

Foundation Model's Embedded Representations May Detect Distribution Shift

Max Vargas, Adam Tsou, Andrew Engel, Tony Chiang

专题命中 指令微调 ：foundation model(title,abstract)；分类 cs.CL、cs.LG

Comments 17 pages, 8 figures, 5 tables

Syllable-level lyrics generation from melody exploiting character-level language model

Zhe Zhang, Karol Lasocki, Yi Yu, Atsuhiro Takasu

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.AI

Fine-Tuning Language Models with Just Forward Passes

Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Comments Accepted by NeurIPS 2023 (oral). Code available at https://github.com/princeton-nlp/MeZO

Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models

Nigel Doering, Cyril Gorlla, Trevor Tuttle, Adhvaith Vijay

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, Jingren Zhou

专题命中 指令微调 ：language model(title,abstract)；分类 cs.CL、cs.LG

Comments The code, checkpoints and demo are released at https://github.com/QwenLM/Qwen-Audio

All in One: Multi-task Prompting for Graph Neural Networks

Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan

专题命中 指令微调 ：prompting(title,abstract)；分类 cs.AI、cs.LG

Comments KDD 23 Best Research Paper Award, which is the first for Hong Kong and Mainland China. A Python Library is released as ProG: https://github.com/sheldonresearch/ProG Submitted to SIGKDD'23 in 03 Feb 2023; Receive Acceptance in 17 May 2023 (Rating 3 4 4 4); Submit to arXiv 1st time in 4 Jul 2023