Fetching the paper…
Reading the bibliography…
Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks.
Effects of varying irrelevant information on adult age differences in problem solving
Hoyer W J, Rebok G W, Sved S M · 1979
Earlier work this paper cites.
Working memory and intrusions of irrelevant information in a group of specific poor problem solvers
Pasolunghi M C, Cornoldi C, De Liberto S · 1999
Earlier work this paper cites.
Learning to solve arithmetic word problems with verb categorization
Hosseini M J, Hajishirzi H, Etzioni O, Kushman N · 2014
Earlier work this paper cites.
Solving general arithmetic word problems
Roy S, Roth D · 2015
Earlier work this paper cites.
Parsing algebraic word problems into equations
Koncel-Kedziorski R, Hajishirzi H, Sabharwal A, Etzioni O, Ang S D · 2015
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling W, Yogatama D, Dyer C, Blunsom P · 2017
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin J, Chang M W, Lee K, Toutanova K · 2019
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Talmor A, Herzig J, Lourie N, Berant J · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown T, Mann B, Ryder N, Subbiah M, Kaplan J D, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D · 2020
Earlier work this paper cites.
Scaling language models: Methods, analysis & insights from training gopher
Rae J W, Borgeaud S, Cai T, Millican K, Hoffmann J, Song F, Aslanides J, Henderson S, Ring R, Young S, others · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Cobbe K, Kosaraju V, Bavarian M, Chen M, Jun H, Kaiser L, Plappert M, Tworek J, Hilton J, Nakano R, Hesse C, Schulman J · 2021
Earlier work this paper cites.
Are NLP models really able to solve simple math word problems?
Patel A, Bhattamishra S, Goyal N · 2021
Earlier work this paper cites.
Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Geva M, Khashabi D, Segal E, Khot T, Roth D, Berant J · 2021
Earlier work this paper cites.
Chain of thought prompting elicits reasoning in large language models
Wei J, Wang X, Schuurmans D, Bosma M, Chi E, Le Q, Zhou D · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Kojima T, Gu S S, Reid M, Matsuo Y, Iwasawa Y · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton F, Miller L, Simens M, Askell A, Welinder P, Christiano P F, Leike J, Lowe R · 2022
Cited alongside, same era.
PaLM: Scaling language modeling with pathways
Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung H W, Sutton C, Gehrmann S, others · 2022
Cited alongside, same era.
Toward efficient language model pretraining and downstream adaptation via self-evolution: A case study on superglue
Zhong Q, Ding L, Zhan Y, Qiao Y, Wen Y, Shen L, Liu J, Yu B, Du B, Chen Y, others · 2022
Cited alongside, same era.
Emergent abilities of large language models
Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D, others · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Kadavath S, Conerly T, Askell A, Henighan T, Drain D, Perez E, Schiefer N, Hatfield-Dodds Z, DasSarma N, Tran-Johnson E, others · 2022
Cited alongside, same era.
Cumulative reasoning with large language models
Zhang Y, Yang J, Yuan Y, Yao A C C · 2023
Later among the works it cites.
Scaling relationship on learning mathematical reasoning with large language models
Yuan Z, Yuan H, Li C, Dong G, Lu K, Tan C, Zhou C, Zhou J · 2023
Later among the works it cites.
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Luo H, Sun Q, Xu C, Zhao P, Lou J, Tao C, Geng X, Lin Q, Chen S, Zhang D · 2023
Later among the works it cites.
Metamath: Bootstrap your own mathematical questions for large language models
Yu L, Jiang W, Shi H, Yu J, Liu Z, Zhang Y, Kwok J T, Li Z, Weller A, Liu W · 2023
Later among the works it cites.
Large language models are reasoning teachers
Ho N, Schmid L, Yun S Y · 2023
Later among the works it cites.
Tinygsm: achieving> 80% on gsm8k with small language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Llama 2: Open foundation and fine-tuned chat models
Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, others · 2023
Cited alongside, same era.
Gpt-4 technical report
OpenAI · 2023
Cited alongside, same era.
Qwen technical report
Bai J, Bai S, Chu Y, Cui Z, Dang K, Deng X, Fan Y, Ge W, Han Y, Huang F, others · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Wang X, Wei J, Schuurmans D, Le Q V, Chi E H, Narang S, Chowdhery A, Zhou D · 2023
Cited alongside, same era.
Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Wang L, Xu W, Lan Y, Hu Z, Lan Y, Lee R K W, Lim E P · 2023
Cited alongside, same era.
Pal: Program-aided language models
Gao L, Madaan A, Zhou S, Alon U, Liu P, Yang Y, Callan J, Neubig G · 2023
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning
Fu Y, Peng H, Sabharwal A, Clark P, Khot T · 2023
Cited alongside, same era.
Liu B, Bubeck S, Eldan R, Kulkarni J, Li Y, Nguyen A, Ward R, Zhang Y · 2023
Later among the works it cites.
Least-to-most prompting enables complex reasoning in large language models
Zhou D, Schärli N, Hou L, Wei J, Scales N, Wang X, Schuurmans D, Cui C, Bousquet O, Le Q V, Chi E H · 2023
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models
Yao S, Yu D, Zhao J, Shafran I, Griffiths T L, Cao Y, Narasimhan K · 2023
Later among the works it cites.
Automatic chain of thought prompting in large language models
Zhang Z, Zhang A, Li M, Smola A · 2023
Later among the works it cites.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Chen W, Ma X, Wang X, Cohen W W · 2023
Later among the works it cites.
Re-reading improves reasoning in language models
Xu X, Tao C, Shen T, Xu C, Xu H, Long G, Lou J g · 2023
Later among the works it cites.
Code llama: Open foundation models for code
Roziere B, Gehring J, Gloeckle F, Sootla S, Gat I, Tan X E, Adi Y, Liu J, Remez T, Rapin J, others · 2023
Later among the works it cites.
Self-refine: Iterative refinement with self-feedback
Madaan A, Tandon N, Gupta P, Hallinan S, Gao L, Wiegreffe S, Alon U, Dziri N, Prabhumoye S, Yang Y, others · 2024
Closest in time.
Automatically correcting large language models: Surveying the landscape of diverse automated correction strategies
Pan L, Saxon M, Xu W, Nathani D, Wang X, Wang W Y · 2024
Closest in time.