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. 2401.05618
  4. Cited By
The Benefits of a Concise Chain of Thought on Problem-Solving in Large
  Language Models

The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models

11 January 2024
Matthew Renze
Erhan Guven
    LRM
ArXivPDFHTML

Papers citing "The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models"

3 / 3 papers shown
Title
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
Aman Madaan
Amir Yazdanbakhsh
LRM
130
115
0
16 Sep 2022
Large Language Models are Zero-Shot Reasoners
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima
S. Gu
Machel Reid
Yutaka Matsuo
Yusuke Iwasawa
ReLM
LRM
291
2,712
0
24 May 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason W. Wei
Xuezhi Wang
Dale Schuurmans
Maarten Bosma
Brian Ichter
F. Xia
Ed H. Chi
Quoc Le
Denny Zhou
LM&Ro
LRM
AI4CE
ReLM
315
8,261
0
28 Jan 2022
1