Papers with Code 아카이브

Mathematical Reasoning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

7

평가표

11

보존된 평가 기록

41

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ensemble-art/Dynamic-Logistic-Ensembles

공식 코드

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification

https://github.com/eth-nlped/scaffolding-generation

공식 코드

Automatic Generation of Socratic Subquestions for Teaching Math Word Problems

https://github.com/eth-sri/matharena

공식 코드

MathArena: Evaluating LLMs on Uncontaminated Math Competitions

https://github.com/ethz-spylab/realmath

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RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematics

https://github.com/evelinehong/LBF

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Learning by Fixing: Solving Math Word Problems with Weak Supervision

https://github.com/facebookresearch/llama

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LLaMA: Open and Efficient Foundation Language Models

https://github.com/fanqiwan/fusellm

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Knowledge Fusion of Large Language Models

https://github.com/fcast07/cqot

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Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying

https://github.com/freedomintelligence/ovm

공식 코드

OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning

https://github.com/freedomintelligence/reasoningnlp

공식 코드

Natural Language Reasoning, A Survey

https://github.com/frenzymath/REAL-Prover

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REAL-Prover: Retrieval Augmented Lean Prover for Mathematical Reasoning

https://github.com/gair-nlp/benbench

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Benchmarking Benchmark Leakage in Large Language Models

https://github.com/gair-nlp/factool

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FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

https://github.com/gair-nlp/limo

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LIMO: Less is More for Reasoning

https://github.com/gair-nlp/limopro

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LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling

https://github.com/gair-nlp/mathpile

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MathPile: A Billion-Token-Scale Pretraining Corpus for Math

https://github.com/gair-nlp/o1-journey

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O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

https://github.com/gair-nlp/realign

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Reformatted Alignment

https://github.com/gair-nlp/reasoneval

공식 코드

Evaluating Mathematical Reasoning Beyond Accuracy

https://github.com/garyjiajia/vrest

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VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism

https://github.com/gauss5930/mclm

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Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning

https://github.com/gblackout/logicllama

공식 코드

Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

https://github.com/gen-verse/reasonflux

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ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates

https://github.com/gen-verse/scoreflow

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ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization

https://github.com/geoeval/geoeval

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GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving

https://github.com/georgao35/marge

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MARGE: Improving Math Reasoning for LLMs with Guided Exploration

https://github.com/gipplab/llm-investig-mathstackexchange

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Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange

https://github.com/gpoesia/peano

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Peano: Learning Formal Mathematical Reasoning

https://github.com/hangeol/unir

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Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs

https://github.com/hanningzhang/er-prm

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Entropy-Regularized Process Reward Model

https://github.com/hbin0701/Self-Explore

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Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards

https://github.com/hendrycks/math

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Measuring Mathematical Problem Solving With the MATH Dataset

https://github.com/hitachi-nlp/fld

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Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

https://github.com/hitsz-tmg/ext-sub

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Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation

https://github.com/hitsz-tmg/visiongraph

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VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context

https://github.com/hiyouga/llama-factory

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Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.