100篇论文纵览语言模型推理能力（论文篇数是什么）

个重要模块，受到了广泛的关注。

近年来，随着技术的不断发展，语言模型的各项能力愈发强大，在部分任务上达到甚至超越了人类水平本文针对语言模型的推理能力，整理了近100篇论文，涉及相关数据集、模型与方法，包含了各大会议及预印本arXiv中的高质量论文。

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目录阅读理解&问答数值推理数学题推理符号推理预训练模型分析与讨论1、阅读理解&问答1.1 相关数据集SQuAD: 100,000+ Questions for Machine Comprehension of Text 【SQuAD数据集】

Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset 【包含详细反馈的数据集】

1.2 相关模型与方法AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension 【将图结构加入到阅读理解任务当中】

Deep Inductive Logic Reasoning for Multi-Hop Reading ComprehensionImproving Machine Reading Comprehension with Contextualized Commonsense Knowledge 【将常识加入到阅读理解任务当中】

Lite Unified Modeling for Discriminative Reading Comprehension 【使用统一的方法建模不同类型的阅读理解任务】Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework 【使用两阶段的方法解决开放域问答任务】

Generated Knowledge Prompting for Commonsense Reasoning 【使用prompt增强常识推理】Modeling Multi-hop Question Answering as Single Sequence Prediction

Open Domain Question Answering with A Unified Knowledge Interface 【使用data-to-text的方式处理开放域问答任务】Program Transfer for Answering Complex Questions over Knowledge Bases 【基于知识库的复杂问答】

Retrieval-guided Counterfactual Generation for QARNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering 【在KBQA领域中使用检索增强答案生成】

Sequence-to-Sequence Knowledge Graph Completion and Question AnsweringSimulating Bandit Learning from User Feedback for Extractive Question Answering 【通过用户反馈进行学习】

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering 【提出了新的子图检索模块】From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension 【将多阶段训练运用到阅读理解任务中】

Language Models of Code are Few-Shot Commonsense Learners 【使用代码相关的预训练语言模型处理常识推理任务】Retrieval Augmentation for Commonsense Reasoning: A Unified Approach 【使用检索增强常识推理任务】

2、数值推理2.1 相关数据集MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data 【财务方面的数据集】

2.2 相关模型与方法ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler 【将符号与数值分离开进行处理】Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems 【优化了数值推理任务中数值的表示方法】

3、数学题推理3.1 相关数据集Are NLP Models really able to Solve Simple Math Word Problems? 【SVAMP英文数据集】Deep Neural Solver for Math Word Problems. 【Math23k中文数据集】

MAWPS: A Math Word Problem Repository 【MAWPS英文数据集】A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers 【ASDiv英文数据集】

Training Verifiers to Solve Math Word Problems 【GSM8k英文数据集】Measuring Mathematical Problem Solving With the MATH Dataset 【MATH英文数据集】

How Well Do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation 【Dophin18k英文数据集】

MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based【MathQA英文数据集】NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks 【NumGLUE数据集】

Lila: A Unified Benchmark for Mathematical Reasoning 【大规模benchmark】Unbiased Math Word Problems Benchmark for Mitigating Solving Bias 【无偏的数学应用题数据集】

3.2 相关模型与方法Mapping to Declarative Knowledge for Word Problem SolvingDeep Neural Solver for Math Word Problems 【使用Seq2Seq模型解决数学问题】

A Goal-Driven Tree-Structured Neural Model for Math Word Problems 【使用树结构XX数学表达式】Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems 【使用树结构XX数学表达式，并在语义上进行对齐】

Graph-to-Tree Learning for Solving Math Word Problems 【使用图结构编码数学题，使用树结构XX数学表达式】LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning 【使用检索公式的方式强化语言模型的数学知识】

Generate & Rank: A Multi-task Framework for Math Word Problems 【使用先生成再排序的方式解决数学问题】On the Advance of Making Language Models Better Reasoners 【使用prompt+verifier的方式增强模型的推理能力】

Tackling Math Word Problems with Fine-to-Coarse Abstracting and Reasoning 【对模型进行不同粒度的训练】Heterogeneous Line Graph Transformer for Math Word Problems 【使用图结构和Transformer解决数学问题】

Improving Compositional Generalization in Math Word Problem Solving 【探究模型在数学题上的组合泛化能力】Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction 【使用关系抽取的方式解决数学问题】

Learning by Fixing: Solving Math Word Problems with Weak Supervision 【弱监督下的数学问题求解】UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression 【对推理过程进行统一】

Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem 【通过多种表达式联合预测数学应用题答案】Solving Math Word Problem via Cooperative Reasoning induced Language Models

Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems 【通过不同的token引导模型生成多样化答案】

Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers 【使用数据增强的方式提升模型的解题能力】

HyperTree Proof Search for Neural Theorem ProvingNaturalProver: Grounded Mathematical Proof Generation with Language Models

Autoformalization with Large Language ModelsThor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

4、代码生成Multilingual Code Snippets Training for Program Translation 【使用多种方式进行数据增强】5、预训练模型Pretrained Language Models are Symbolic Mathematics Solvers too!

Large Language Models are Zero-Shot Reasoners 【使用Chain-of-Though增强大规模模型的推理能力】Self-Consistency Improves Chain of Thought Reasoning in Language Models

SHOW YOUR WORK: SCRATCHPADS FOR INTERMEDIATE COMPUTATION WITH LANGUAGE MODELS 【使用草稿纸提升模型的推理能力】Autoformalization with Large Language Models

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem SolvingCodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Unsupervised Translation of Programming Languages 【使用翻译的方式增强预训练】Solving Quantitative Reasoning Problems with Language Models 【使用网页和arXiv上的文章进行预训练】

reStructured Pre-training 【使用prompt的方式统一预训练任务】A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level 【使用模型将根据题目生成代码，然后运行代码得到答案】

MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems 【使用共享Encoder进行多任务预训练】Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network 【基于语义图结构的预训练模型】

JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding 【基于数学语料的中文预训练模型】

LinkBERT: Pretraining Language Models with Document Links 【使用文档链接增强模型对不同知识点的关联能力】Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks 【使用代码代替CoT的中间过程】

CBEAF-Adapting: Enhanced Continual Pretraining for Building Chinese Biomedical Language Model 【添加少量参数使得PLM能够快速进行新领域的预训练】

STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning 【模型对自己产生的负例进行学习】Complexity-Based Prompting for Multi-step Reasoning 【使用prompt技术增强大模型的推理能力】

6、分析与讨论Language models show human-like content effects on reasoning 【讨论模型推理能力的局限性】ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models 【分析预训练语言模型的各项能力】

Exploring Length Generalization in Large Language Models 【分析模型在推理中长度泛化问题】Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers 【数学应用题相关综述】

What Makes Reading Comprehension Questions Difficult? 【分析模型在不同推理类型的阅读理解任务上的表现】How Do We Answer Complex Questions: Discourse Structure of Long-form Answers 【分析模型对于长答案的处理】

Do Language Models Understand Measurements? 【分析语言模型对数值的理解能力】Investigating Math Word Problems using Pretrained Multilingual Language Models 【探究多语预训练语言模型对于数学应用题的解题能力】

Limitations of Language Models in Arithmetic and Symbolic Induction 【模型在推理任务中的局限性】A Systematic Investigation of Commonsense Knowledge in Large Language Models 【评估语言模型的常识推理能力】