Papers with Code 아카이브

Mathematical Reasoning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

11

보존된 평가 기록

41

코드

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

https://github.com/neurasearch/neurips-2022-submission-3358

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ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler

https://github.com/nianqi-li/multilingpot

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MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning

https://github.com/nikhilsab/h-star

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H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables

https://github.com/njunlp/mapo

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MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization

https://github.com/njunlp/qalign

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Question Translation Training for Better Multilingual Reasoning

https://github.com/njunlp/r-prm

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R-PRM: Reasoning-Driven Process Reward Modeling

https://github.com/nlpcode/tubench

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TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions

https://github.com/nusnlp/fspo

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The Hallucination Dilemma: Factuality-Aware Reinforcement Learning for Large Reasoning Models

https://github.com/oashua/mathagent

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Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent

https://github.com/oceanypt/TOA

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Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration

https://github.com/ofa-sys/gsm8k-screl

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Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

https://github.com/om-ai-lab/open-agent-leaderboard

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Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research

https://github.com/open-compass/opencompass

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NeedleBench: Can LLMs Do Retrieval and Reasoning in Information-Dense Context?

https://github.com/openai/grade-school-math

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Training Verifiers to Solve Math Word Problems

https://github.com/openai/prm800k

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Let's Verify Step by Step

https://github.com/optimization-ai/disco

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DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization

https://github.com/osu-nlp-group/llm-planning-eval

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When is Tree Search Useful for LLM Planning? It Depends on the Discriminator

https://github.com/paperswithcode/galai

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Galactica: A Large Language Model for Science

https://github.com/pdx97/sbi-rag_neurips2024

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SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation

https://github.com/peizhengwang/partialordinalshapley

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Data valuation: The partial ordinal Shapley value for machine learning

https://github.com/pengshuai-rin/multimath

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MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models

https://github.com/polymathbenchmark/PolyMATH

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Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark

https://github.com/pomonam/kronfluence

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Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

https://github.com/princeton-vl/CoqGym

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Learning to Prove Theorems via Interacting with Proof Assistants

https://github.com/princeton-vl/MetaQNL.jl

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Learning Symbolic Rules for Reasoning in Quasi-Natural Language

https://github.com/princeton-vl/metaqnl

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Learning Symbolic Rules for Reasoning in Quasi-Natural Language

https://github.com/psunlpgroup/fover

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Training Step-Level Reasoning Verifiers with Formal Verification Tools

https://github.com/qingyangzhang/empo

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Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization

https://github.com/qizhipei/mathfusion

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MathFusion: Enhancing Mathematic Problem-solving of LLM through Instruction Fusion

https://github.com/qtli/gsm-plus

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GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers

https://github.com/quinn777/atomthink

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AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning

https://github.com/qwenlm/processbench

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ProcessBench: Identifying Process Errors in Mathematical Reasoning

https://github.com/qwenlm/qwen2

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Qwen2 Technical Report

https://github.com/qwenlm/qwen2.5

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Qwen2.5 Technical Report

https://github.com/qwenlm/qwen3

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Qwen3 Technical Report

https://github.com/raghavsinghal10/lora-sb

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Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning

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