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. 2406.13131
  4. Cited By
When Parts are Greater Than Sums: Individual LLM Components Can
  Outperform Full Models

When Parts are Greater Than Sums: Individual LLM Components Can Outperform Full Models

19 June 2024
Ting-Yun Chang
Jesse Thomason
Robin Jia
ArXivPDFHTML

Papers citing "When Parts are Greater Than Sums: Individual LLM Components Can Outperform Full Models"

13 / 13 papers shown
Title
Decomposing and Editing Predictions by Modeling Model Computation
Decomposing and Editing Predictions by Modeling Model Computation
Harshay Shah
Andrew Ilyas
A. Madry
KELM
26
4
0
17 Apr 2024
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Wes Gurnee
Neel Nanda
Matthew Pauly
Katherine Harvey
Dmitrii Troitskii
Dimitris Bertsimas
MILM
153
170
0
02 May 2023
Dissecting Recall of Factual Associations in Auto-Regressive Language
  Models
Dissecting Recall of Factual Associations in Auto-Regressive Language Models
Mor Geva
Jasmijn Bastings
Katja Filippova
Amir Globerson
KELM
183
260
0
28 Apr 2023
Interpretability in the Wild: a Circuit for Indirect Object
  Identification in GPT-2 small
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Kevin Wang
Alexandre Variengien
Arthur Conmy
Buck Shlegeris
Jacob Steinhardt
205
486
0
01 Nov 2022
Ask Me Anything: A simple strategy for prompting language models
Ask Me Anything: A simple strategy for prompting language models
Simran Arora
A. Narayan
Mayee F. Chen
Laurel J. Orr
Neel Guha
Kush S. Bhatia
Ines Chami
Frederic Sala
Christopher Ré
ReLM
LRM
190
160
0
05 Oct 2022
Complexity-Based Prompting for Multi-Step Reasoning
Complexity-Based Prompting for Multi-Step Reasoning
Yao Fu
Hao-Chun Peng
Ashish Sabharwal
Peter Clark
Tushar Khot
ReLM
LRM
152
298
0
03 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
178
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
PromptSource: An Integrated Development Environment and Repository for
  Natural Language Prompts
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen H. Bach
Victor Sanh
Zheng-Xin Yong
Albert Webson
Colin Raffel
...
Khalid Almubarak
Xiangru Tang
Dragomir R. Radev
Mike Tian-Jian Jiang
Alexander M. Rush
VLM
212
335
0
02 Feb 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
882
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
Making Pre-trained Language Models Better Few-shot Learners
Making Pre-trained Language Models Better Few-shot Learners
Tianyu Gao
Adam Fisch
Danqi Chen
238
1,898
0
31 Dec 2020
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
6,003
0
20 Apr 2018
1