ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2307.12375
  4. Cited By
In-Context Learning Learns Label Relationships but Is Not Conventional
  Learning

In-Context Learning Learns Label Relationships but Is Not Conventional Learning

23 July 2023
Jannik Kossen
Y. Gal
Tom Rainforth
ArXivPDFHTML

Papers citing "In-Context Learning Learns Label Relationships but Is Not Conventional Learning"

27 / 27 papers shown
Title
Incomplete In-context Learning
Incomplete In-context Learning
Wenqiang Wang
Yangshijie Zhang
26
0
0
12 May 2025
Rethinking Invariance in In-context Learning
Rethinking Invariance in In-context Learning
Lizhe Fang
Yifei Wang
Khashayar Gatmiry
Lei Fang
Y. Wang
39
1
0
08 May 2025
Shortcut Learning in In-Context Learning: A Survey
Shortcut Learning in In-Context Learning: A Survey
Rui Song
Yingji Li
Fausto Giunchiglia
Fausto Giunchiglia
Hao Xu
35
1
0
04 Nov 2024
Can In-context Learning Really Generalize to Out-of-distribution Tasks?
Can In-context Learning Really Generalize to Out-of-distribution Tasks?
Qixun Wang
Yifei Wang
Yisen Wang
Xianghua Ying
OOD
23
2
0
13 Oct 2024
Wrong-of-Thought: An Integrated Reasoning Framework with
  Multi-Perspective Verification and Wrong Information
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information
Yongheng Zhang
Qiguang Chen
Jingxuan Zhou
Peng Wang
Jiasheng Si
Jin Wang
Wenpeng Lu
Libo Qin
LRM
44
3
0
06 Oct 2024
Disentangling Latent Shifts of In-Context Learning Through Self-Training
Disentangling Latent Shifts of In-Context Learning Through Self-Training
Josip Jukić
Jan Snajder
21
0
0
02 Oct 2024
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought:
  Probability, Memorization, and Noisy Reasoning
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning
Akshara Prabhakar
Thomas L. Griffiths
R. Thomas McCoy
LRM
34
16
0
01 Jul 2024
UnUnlearning: Unlearning is not sufficient for content regulation in
  advanced generative AI
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
Ilia Shumailov
Jamie Hayes
Eleni Triantafillou
Guillermo Ortiz-Jimenez
Nicolas Papernot
Matthew Jagielski
Itay Yona
Heidi Howard
Eugene Bagdasaryan
MU
18
19
0
27 Jun 2024
Estimating the Hallucination Rate of Generative AI
Estimating the Hallucination Rate of Generative AI
Andrew Jesson
Nicolas Beltran-Velez
Quentin Chu
Sweta Karlekar
Jannik Kossen
Yarin Gal
John P. Cunningham
David M. Blei
35
6
0
11 Jun 2024
What Do Language Models Learn in Context? The Structured Task Hypothesis
What Do Language Models Learn in Context? The Structured Task Hypothesis
Jiaoda Li
Yifan Hou
Mrinmaya Sachan
Ryan Cotterell
LRM
31
7
0
06 Jun 2024
On the Noise Robustness of In-Context Learning for Text Generation
On the Noise Robustness of In-Context Learning for Text Generation
Hongfu Gao
Feipeng Zhang
Wenyu Jiang
Jun Shu
Feng Zheng
Hongxin Wei
48
3
0
27 May 2024
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?
Chengwei Qin
Wenhan Xia
Tan Wang
Fangkai Jiao
Yuchen Hu
Bosheng Ding
Ruirui Chen
Shafiq R. Joty
LRM
35
3
0
19 Apr 2024
Many-Shot In-Context Learning
Many-Shot In-Context Learning
Rishabh Agarwal
Avi Singh
Lei M. Zhang
Bernd Bohnet
Luis Rosias
...
John D. Co-Reyes
Eric Chu
Feryal M. P. Behbahani
Aleksandra Faust
Hugo Larochelle
ReLM
OffRL
BDL
47
96
0
17 Apr 2024
From Words to Numbers: Your Large Language Model Is Secretly A Capable
  Regressor When Given In-Context Examples
From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples
Robert Vacareanu
Vlad-Andrei Negru
Vasile Suciu
Mihai Surdeanu
23
4
0
11 Apr 2024
Learning to Poison Large Language Models During Instruction Tuning
Learning to Poison Large Language Models During Instruction Tuning
Yao Qiang
Xiangyu Zhou
Saleh Zare Zade
Mohammad Amin Roshani
Douglas Zytko
Dongxiao Zhu
AAML
SILM
27
20
0
21 Feb 2024
An Empirical Study of In-context Learning in LLMs for Machine
  Translation
An Empirical Study of In-context Learning in LLMs for Machine Translation
Pranjal A. Chitale
Jay Gala
Raj Dabre
LRM
13
5
0
22 Jan 2024
In-context Learning with Retrieved Demonstrations for Language Models: A
  Survey
In-context Learning with Retrieved Demonstrations for Language Models: A Survey
an Luo
Xin Xu
Yue Liu
Panupong Pasupat
Mehran Kazemi
RALM
24
54
0
21 Jan 2024
Hijacking Large Language Models via Adversarial In-Context Learning
Hijacking Large Language Models via Adversarial In-Context Learning
Yao Qiang
Xiangyu Zhou
Dongxiao Zhu
30
32
0
16 Nov 2023
The Mystery of In-Context Learning: A Comprehensive Survey on
  Interpretation and Analysis
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
Yuxiang Zhou
Jiazheng Li
Yanzheng Xiang
Hanqi Yan
Lin Gui
Yulan He
22
13
0
01 Nov 2023
The Learnability of In-Context Learning
The Learnability of In-Context Learning
Noam Wies
Yoav Levine
Amnon Shashua
110
89
0
14 Mar 2023
Transformers generalize differently from information stored in context
  vs in weights
Transformers generalize differently from information stored in context vs in weights
Stephanie C. Y. Chan
Ishita Dasgupta
Junkyung Kim
D. Kumaran
Andrew Kyle Lampinen
Felix Hill
98
45
0
11 Oct 2022
On the Relation between Sensitivity and Accuracy in In-context Learning
On the Relation between Sensitivity and Accuracy in In-context Learning
Yanda Chen
Chen Zhao
Zhou Yu
Kathleen McKeown
He He
180
77
0
16 Sep 2022
Training language models to follow instructions with human feedback
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
301
11,730
0
04 Mar 2022
Fantastically Ordered Prompts and Where to Find Them: Overcoming
  Few-Shot Prompt Order Sensitivity
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Yao Lu
Max Bartolo
Alastair Moore
Sebastian Riedel
Pontus Stenetorp
AILaw
LRM
274
1,114
0
18 Apr 2021
What Makes Good In-Context Examples for GPT-$3$?
What Makes Good In-Context Examples for GPT-333?
Jiachang Liu
Dinghan Shen
Yizhe Zhang
Bill Dolan
Lawrence Carin
Weizhu Chen
AAML
RALM
275
1,296
0
17 Jan 2021
Fine-Tuning Language Models from Human Preferences
Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler
Nisan Stiennon
Jeff Wu
Tom B. Brown
Alec Radford
Dario Amodei
Paul Christiano
G. Irving
ALM
275
1,561
0
18 Sep 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
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
294
6,927
0
20 Apr 2018
1