Papers with Code · CC BY-SA 4.0

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아카이브 평가 26건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
GPT-4 (Teaching-Inspired)	Execution Accuracy 93.9	Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models 2024-10-10	코드 1
GPT-4 (Model Selection)	Execution Accuracy 93.7	Automatic Model Selection with Large Language Models for Reasoning 2023-05-23	코드 1
Qwen2(CoT + Code Interpreter)	Execution Accuracy 92.3	연결 없음 논문 날짜 미기재	연결 없음
GPT-4 (PHP)	Execution Accuracy 91.9	Progressive-Hint Prompting Improves Reasoning in Large Language Models 2023-04-19	코드 1
OpenMath-CodeLlama-70B (w/ code)	Execution Accuracy 87.8	OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset 2024-02-15	코드 1
MathCoder-L-70B	Execution Accuracy 84.9	MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning 2023-10-05	코드 1
PoT_Eng (self-consistency @ 5)	Execution Accuracy 83.70	Does ChatGPT Comprehend the Place Value in Numbers When Solving Math Word Problems? 2023-06-03	코드 1
CoT_Eng (self-consistency @ 5)	Execution Accuracy 82.50	Does ChatGPT Comprehend the Place Value in Numbers When Solving Math Word Problems? 2023-06-03	코드 1
MMOS-CODE-34B(0-shot)	Execution Accuracy 80.6	An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning 2024-02-23	코드 1
MMOS-DeepSeekMath-7B(0-shot)	Execution Accuracy 79.3	An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning 2024-02-23	코드 1
MMOS-CODE-7B(0-shot)	Execution Accuracy 76.4	An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning 2024-02-23	코드 1
LLaMA 2-Chat	Execution Accuracy 69.2	Llama 2: Open Foundation and Fine-Tuned Chat Models 2023-07-18	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 · 코드 17 · 코드 18 · 코드 19
DeBERTa	Execution Accuracy 63.5 Accuracy 63.5	Math Word Problem Solving by Generating Linguistic Variants of Problem Statements 2023-06-24	코드 1
PaLM (zero-shot, CoT)	Execution Accuracy 62.1	Large Language Models are Zero-Shot Reasoners 2022-05-24	코드 1 · 코드 2 · 코드 3 · 코드 4
PaLM (zero-shot)	Execution Accuracy 58.8	Large Language Models are Zero-Shot Reasoners 2022-05-24	코드 1 · 코드 2 · 코드 3 · 코드 4
SYRELM (Vicuna 13B)	Execution Accuracy 56.65	Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning 2023-12-09	코드 1
ATHENA (roberta-large)	Execution Accuracy 54.8	ATHENA: Mathematical Reasoning with Thought Expansion 2023-11-02	코드 1
MsAT-DeductReasoner	Execution Accuracy 48.9	Learning Multi-Step Reasoning by Solving Arithmetic Tasks 2023-06-02	코드 1
Roberta-DeductReasoner	Execution Accuracy 47.3	Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction 2022-03-19	코드 1
ATHENA (roberta-base)	Execution Accuracy 45.6	ATHENA: Mathematical Reasoning with Thought Expansion 2023-11-02	코드 1
Graph2Tree with RoBERTa	Execution Accuracy 43.8 Accuracy 43.8	Are NLP Models really able to Solve Simple Math Word Problems? 2021-03-12	코드 1 · 코드 2 · 코드 3
GTS with RoBERTa	Execution Accuracy 41.0 Accuracy 41.0	Are NLP Models really able to Solve Simple Math Word Problems? 2021-03-12	코드 1 · 코드 2 · 코드 3
LSTM Seq2Seq with RoBERTa	Execution Accuracy 40.3 Accuracy 40.3	Are NLP Models really able to Solve Simple Math Word Problems? 2021-03-12	코드 1 · 코드 2 · 코드 3
SYRELM (GPT-J)	Execution Accuracy 40.1	Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning 2023-12-09	코드 1
Transformer with RoBERTa	Execution Accuracy 38.9 Accuracy 38.9	Are NLP Models really able to Solve Simple Math Word Problems? 2021-03-12	코드 1 · 코드 2 · 코드 3
GPT-4 DUP	Accuracy 94.2	Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems 2024-04-23	코드 1