Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models

24 August 2022

Papers citing "Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models"

6 / 6 papers shown

Title
Faith and Fate: Limits of Transformers on Compositionality Nouha Dziri Ximing Lu Melanie Sclar Xiang Lorraine Li Liwei Jian ... Sean Welleck Xiang Ren Allyson Ettinger Zaïd Harchaoui Yejin Choi ReLM LRM 30 329 0 29 May 2023
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data Carlos Gemmell Jeffrey Stephen Dalton LMTD 30 13 0 17 Mar 2023
Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang Jason W. Wei Dale Schuurmans Quoc Le Ed H. Chi Sharan Narang Aakanksha Chowdhery Denny Zhou ReLM BDL LRM AI4CE 314 3,248 0 21 Mar 2022
Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks Wang Zhu Peter Shaw Tal Linzen Fei Sha 35 7 0 09 Nov 2021
Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model Kuntal Kumar Pal Chitta Baral 105 18 0 10 Sep 2021
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation Ofir Press Noah A. Smith M. Lewis 253 695 0 27 Aug 2021