Built on

Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Original
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 1905
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
Earlier work this paper cites.
Python reference manual
Guido Van Rossum and Fred L Drake Jr · 1995
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2005
Earlier work this paper cites.
An Elementary Introduction to the Wolfram Language
Stephen Wolfram · 2015
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg · 2017
Earlier work this paper cites.
Training verifiers to solve math word problems
Original
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Original
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
Earlier work this paper cites.
Show your work: Scratchpads for intermediate computation with language models
Original
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
Earlier work this paper cites.
Are nlp models really able to solve simple math word problems?
Original
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
Earlier work this paper cites.

Similar

Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Original
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
Cited alongside, same era.
A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Original
Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, et al · 2022
Cited alongside, same era.
Roscoe: A suite of metrics for scoring step-by-step reasoning
Original
Olga Golovneva, Moya Peng Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz · 2022
Cited alongside, same era.
Structured prompting: Scaling in-context learning to 1,000 examples
Original
Yaru Hao, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, and Furu Wei · 2022
Cited alongside, same era.

Then

Solving math word problems with process-and outcome-based feedback
Original
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
Later among the works it cites.
Meta-in-context learning in large language models
Original
Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matthew Botvinick, Jane X Wang, and Eric Schulz · 2023
Closest in time.
Complexity-based prompting for multi-step reasoning
Original
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot · 2023
Closest in time.
PAL: Program-aided language models
Original
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
Closest in time.
Solving math word problems by combining language models with symbolic solvers

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2023

Design of Chain-of-Thought in Math Problem Solving

Jie, Zhanming, Luong, Trung Quoc, Zhang, Xinbo et al.

Understand

Chain-of-Thought (CoT) plays a crucial role in reasoning for math problem solving.

We conduct a comprehensive examination of methods for designing CoT, comparing conventional natural language CoT with various program CoTs, including the self-describing program, the comment-describing program, and the non-describing program.
Furthermore, we investigate the impact of programming language on program CoTs, comparing Python and Wolfram Language.
Through extensive experiments on GSM8K, MATHQA, and SVAMP, we find that program CoTs often have superior effectiveness in math problem solving.

Built on

Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Original
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 1905
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
Earlier work this paper cites.
Python reference manual
Guido Van Rossum and Fred L Drake Jr · 1995
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2005
Earlier work this paper cites.
An Elementary Introduction to the Wolfram Language
Stephen Wolfram · 2015
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg · 2017
Earlier work this paper cites.
Training verifiers to solve math word problems
Original
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Original
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
Earlier work this paper cites.
Show your work: Scratchpads for intermediate computation with language models
Original
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
Earlier work this paper cites.
Are nlp models really able to solve simple math word problems?
Original
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
Earlier work this paper cites.

Similar

Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Original
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
Cited alongside, same era.
A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Original
Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, et al · 2022
Cited alongside, same era.
Roscoe: A suite of metrics for scoring step-by-step reasoning
Original
Olga Golovneva, Moya Peng Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz · 2022
Cited alongside, same era.
Structured prompting: Scaling in-context learning to 1,000 examples
Original
Yaru Hao, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, and Furu Wei · 2022
Cited alongside, same era.

Then

Solving math word problems with process-and outcome-based feedback
Original
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
Later among the works it cites.
Meta-in-context learning in large language models
Original
Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matthew Botvinick, Jane X Wang, and Eric Schulz · 2023
Closest in time.
Complexity-based prompting for multi-step reasoning
Original
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot · 2023
Closest in time.
PAL: Program-aided language models
Original
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
Closest in time.
Solving math word problems by combining language models with symbolic solvers

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Learning to reason deductively: Math word problem solving as complex relation extraction

Zhanming Jie, Jierui Li, and Wei Lu · 2022

Cited alongside, same era.

Competition-level code generation with alphacode

Original

Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022

Cited alongside, same era.

What makes good in-context examples for gpt- 3 3 ?

Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2022

Cited alongside, same era.

Metaicl: Learning to learn in context

Original

Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2022

Cited alongside, same era.

Bloom: A 176b-parameter open-access multilingual language model

Original

Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al · 2022

Cited alongside, same era.

Investigating math word problems using pretrained multilingual language models

Minghuan Tan, Lei Wang, Lingxiao Jiang, and Jing Jiang · 2022

Cited alongside, same era.

Galactica: A large language model for science

Original

Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022

Cited alongside, same era.

Llama: Open and efficient foundation language models

Original

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al

Cited in the paper.

Original

Joy He-Yueya, Gabriel Poesia, Rose E Wang, and Noah D Goodman · 2023

Closest in time.

Mathprompter: Mathematical reasoning using large language models

Original

Shima Imani, Liang Du, and Harsh Shrivastava · 2023

Closest in time.

Decomposed prompting: A modular approach for solving complex tasks

Original

Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 2023

Closest in time.