超出实证机器学习的论文类型

本指南旨在帮助撰写非标准类型的论文：理论论文、综述/教程类论文、基准/数据集论文以及立场声明类论文。不同类型的论文在结构、证据要求以及目标发表平台方面各有差异。

目录

• 理论论文
• 综述与教程类论文
• 基准与数据集论文
• 立场声明类论文
• 可复现性及重复实验论文

理论论文

何时撰写理论论文

当满足以下条件时，您的论文应归类为理论论文：

• 主要贡献为某个定理、界限、不可能性结论或形式化描述
• 实验仅作为补充验证手段，而非核心证据
• 该研究旨在深化理论理解，而非单纯追求最优性能指标

论文结构

1. Introduction (1-1.5 pages)
   - Problem statement and motivation
   - Informal statement of main results
   - Comparison to prior theoretical work
   - Contribution bullets (state theorems informally)

2. Preliminaries (0.5-1 page)
   - Notation table
   - Formal definitions
   - Assumptions (numbered, referenced later)
   - Known results you build on

3. Main Results (2-3 pages)
   - Theorem statements (formal)
   - Proof sketches (intuition + key steps)
   - Corollaries and special cases
   - Discussion of tightness / optimality

4. Experimental Validation (1-2 pages, optional but recommended)
   - Do theoretical predictions match empirical behavior?
   - Synthetic experiments that isolate the phenomenon
   - Comparison to bounds from prior work

5. Related Work (1 page)
   - Theoretical predecessors
   - Empirical work your theory explains

6. Discussion & Open Problems (0.5 page)
   - Limitations of your results
   - Conjectures suggested by your analysis
   - Concrete open problems

Appendix:
   - Full proofs
   - Technical lemmas
   - Extended experimental details

编写定理

表述清晰的定理模板：

\begin{assumption}[Bounded Gradients]\label{assum:bounded-grad}
There exists $G > 0$ such that $\|\nabla f(x)\| \leq G$ for all $x \in \mathcal{X}$.
\end{assumption}

\begin{theorem}[Convergence Rate]\label{thm:convergence}
Under Assumptions~\ref{assum:bounded-grad} and~\ref{assum:smoothness},
Algorithm~\ref{alg:method} with step size $\eta = \frac{1}{\sqrt{T}}$ satisfies
\[
\frac{1}{T}\sum_{t=1}^{T} \mathbb{E}\left[\|\nabla f(x_t)\|^2\right]
\leq \frac{2(f(x_1) - f^*)}{\sqrt{T}} + \frac{G^2}{\sqrt{T}}.
\]
In particular, after $T = O(1/\epsilon^2)$ iterations, we obtain an
$\epsilon$-stationary point.
\end{theorem}

定理陈述的规则：

• 明确列出所有假设（需编号并标注名称）
• 需给出正式的界，而不仅仅是“以 O(·) 的速率收敛”
• 添加通俗易懂的推论说明：“具体而言，这意味着……”
• 与已有界进行比较：“相较于[先前研究]中 O(·) 的界，该结果提升了……倍”

证明概要

在理论论文的正文中，证明概要是最为重要的部分。审稿人会通过它来判断你的工作是否具备真正的洞察力，还是仅仅是机械式的推导。

优秀的证明概要结构：

\begin{proof}[Proof Sketch of Theorem~\ref{thm:convergence}]
The key insight is that [one sentence describing the main idea].

The proof proceeds in three steps:
\begin{enumerate}
\item \textbf{Decomposition.} We decompose the error into [term A]
  and [term B] using [technique]. This reduces the problem to
  bounding each term separately.

\item \textbf{Bounding [term A].} By [assumption/lemma], [term A]
  is bounded by $O(\cdot)$. The critical observation is that
  [specific insight that makes this non-trivial].

\item \textbf{Combining.} Choosing $\eta = 1/\sqrt{T}$ balances
  the two terms, yielding the stated bound.
\end{enumerate}

The full proof, including the technical lemma for Step 2,
appears in Appendix~\ref{app:proofs}.
\end{proof}

劣质证明草稿：仅用略有不同的符号重新表述定理，或简单声明“该证明采用了标准方法”。

完整证明见附录

\appendix
\section{Proofs}\label{app:proofs}

\subsection{Proof of Theorem~\ref{thm:convergence}}

We first establish two technical lemmas.

\begin{lemma}[Descent Lemma]\label{lem:descent}
Under Assumption~\ref{assum:smoothness}, for any step size $\eta \leq 1/L$:
\[
f(x_{t+1}) \leq f(x_t) - \frac{\eta}{2}\|\nabla f(x_t)\|^2 + \frac{\eta^2 L}{2}\|\nabla f(x_t)\|^2.
\]
\end{lemma}

\begin{proof}
[Complete proof with all steps]
\end{proof}

% Continue with remaining lemmas and main theorem proof

理论论文常见的误区

理论论文的发表平台

综述论文与教程论文

何时撰写综述论文

• 某个子领域已发展成熟，需要进行综合梳理
• 您发现了单篇论文未能揭示的各研究工作之间的关联
• 该领域的初学者缺乏合适的入门资料
• 自上一篇综述发表以来，该领域的研究格局发生了显著变化

注意：撰写综述需要深厚的专业功底。即使内容极为全面，但由领域外人士撰写的综述也难以把握细节，且可能对相关研究产生误判。

论文结构

1. Introduction (1-2 pages)
   - Scope definition (what's included and excluded, and why)
   - Motivation for the survey now
   - Overview of organization (often with a figure)

2. Background / Problem Formulation (1-2 pages)
   - Formal problem definition
   - Notation (used consistently throughout)
   - Historical context

3. Taxonomy (the core contribution)
   - Organize methods along meaningful axes
   - Present taxonomy as a figure or table
   - Each category gets a subsection

4. Detailed Coverage (bulk of paper)
   - For each category: representative methods, key ideas, strengths/weaknesses
   - Comparison tables within and across categories
   - Don't just describe — analyze and compare

5. Experimental Comparison (if applicable)
   - Standardized benchmark comparison
   - Fair hyperparameter tuning for all methods
   - Not always feasible but significantly strengthens the survey

6. Open Problems & Future Directions (1-2 pages)
   - Unsolved problems the field should tackle
   - Promising but underexplored directions
   - This section is what makes a survey a genuine contribution

7. Conclusion

分类体系设计

分类体系是调研报告的核心价值所在。它应当满足以下要求：

• 具有实际意义：各类别应对应真实的方法学差异，而非随意划分的群体
• 覆盖全面：所有相关论文都应有归属之处
• 尽可能互斥：每篇论文仅属于一个主要类别
• 名称清晰明确：使用“基于注意力机制的方法”而非“第3类”
• 便于可视化呈现：附上分类体系示意图通常能极大提升可读性

以“大语言模型推理”主题调研为例的分类维度：

• 按技术路线划分：思维链法、思维树法、自我一致性方法、工具使用方法
• 按训练要求划分：仅通过提示训练、微调模型、基于强化学习的人类反馈优化
• 按推理类型划分：数学推理、常识推理、逻辑推理、因果推理

撰写标准

• 引用所有相关论文——作者需核查自己的研究是否被纳入评估
• 保持客观公正——避免因个人偏好而否定某些方法
• 进行综合分析而非简单罗列——提炼出共性规律、权衡因素及未解问题
• 添加对比表格——即使是定性对比（如功能/特性清单）也可
• 提交前及时更新——检查自撰写开始后发表在arXiv上的新论文

调研报告的发表渠道

基准测试与数据集相关论文

何时撰写基准测试论文

• 现有基准测试无法衡量您认为重要的指标
• 出现了新的能力，但目前尚无标准评估方法
• 现有基准测试已趋于饱和（所有方法的得分均超过95%）
• 您希望为某个发展较为分散的子领域建立统一的评估标准

论文结构

1. Introduction
   - What evaluation gap does this benchmark fill?
   - Why existing benchmarks are insufficient

2. Task Definition
   - Formal task specification
   - Input/output format
   - Evaluation criteria (what makes a good answer?)

3. Dataset Construction
   - Data source and collection methodology
   - Annotation process (if human-annotated)
   - Quality control measures
   - Dataset statistics (size, distribution, splits)

4. Baseline Evaluation
   - Run strong baselines (don't just report random/majority)
   - Show the benchmark is challenging but not impossible
   - Human performance baseline (if feasible)

5. Analysis
   - Error analysis on baselines
   - What makes items hard/easy?
   - Construct validity: does the benchmark measure what you claim?

6. Intended Use & Limitations
   - What should this benchmark be used for?
   - What should it NOT be used for?
   - Known biases or limitations

7. Datasheet (Appendix)
   - Full datasheet for datasets (Gebru et al.)

评估标准

审稿人对基准测试的评估标准与方法类论文有所不同：

数据集文档要求

请遵循《数据集数据表框架》（Gebru等人，2021年）：

Datasheet Questions:
1. Motivation
   - Why was this dataset created?
   - Who created it and on behalf of whom?
   - Who funded the creation?

2. Composition
   - What do the instances represent?
   - How many instances are there?
   - Does it contain all possible instances or a sample?
   - Is there a label? If so, how was it determined?
   - Are there recommended data splits?

3. Collection Process
   - How was the data collected?
   - Who was involved in collection?
   - Over what timeframe?
   - Was ethical review conducted?

4. Preprocessing
   - What preprocessing was done?
   - Was the "raw" data saved?

5. Uses
   - What tasks has this been used for?
   - What should it NOT be used for?
   - Are there other tasks it could be used for?

6. Distribution
   - How is it distributed?
   - Under what license?
   - Are there any restrictions?

7. Maintenance
   - Who maintains it?
   - How can users contact the maintainer?
   - Will it be updated? How?
   - Is there an erratum?

基准测试论文的发表平台

观点论文

何时撰写观点论文

• 您对该领域的未来发展有独到见解
• 您希望挑战某种普遍存在的假设
• 您希望基于分析结果提出研究规划
• 您发现了当前研究方法中存在的系统性问题

论文结构

1. Introduction
   - State your thesis clearly in the first paragraph
   - Why this matters now

2. Background
   - Current state of the field
   - Prevailing assumptions you're challenging

3. Argument
   - Present your thesis with supporting evidence
   - Evidence can be: empirical data, theoretical analysis, logical argument,
     case studies, historical precedent
   - Be rigorous — this isn't an opinion piece

4. Counterarguments
   - Engage seriously with the strongest objections
   - Explain why they don't undermine your thesis
   - Concede where appropriate — it strengthens credibility

5. Implications
   - What should the field do differently?
   - Concrete research directions your thesis suggests
   - How should evaluation/methodology change?

6. Conclusion
   - Restate thesis
   - Call to action

撰写标准

• 首先提出最有力的论点——不要在首段含糊其辞
• 诚实地回应反对意见——优秀的立场论文会针对最有力的质疑进行回应，而非最微不足道的
• 提供证据——没有证据的立场论文不过是主观评论
• 表述具体——“该领域应采取X措施”比“还需要更多工作”更佳
• 避免歪曲现有研究——客观公正地描述对立观点

适合发表立场论文的会议

可复现性及重复实验论文

何时撰写可复现性论文

• 你尝试复现某项已发表的研究结果，并取得了成功或失败
• 你想在不同条件下验证相关结论
• 你发现某种流行方法的性能取决于未公开的细节

论文结构

1. Introduction
   - What paper/result are you reproducing?
   - Why is this reproduction valuable?

2. Original Claims
   - State the exact claims from the original paper
   - What evidence was provided?

3. Methodology
   - Your reproduction approach
   - Differences from original (if any) and why
   - What information was missing from the original paper?

4. Results
   - Side-by-side comparison with original results
   - Statistical comparison (confidence intervals overlap?)
   - What reproduced and what didn't?

5. Analysis
   - If results differ: why? What's sensitive?
   - Hidden hyperparameters or implementation details?
   - Robustness to seed, hardware, library versions?

6. Recommendations
   - For original authors: what should be clarified?
   - For practitioners: what to watch out for?
   - For the field: what reproducibility lessons emerge?

活动与平台