03. Related Work

Chapter 3 of 18 · 10 min

KEY INSIGHT

Related work sections fail when they merely list papers; they succeed when they position your contribution as the logical next step in a progression of ideas. A well-written related work section accomplishes three goals: (1) establishes the current state of the art, (2) identifies gaps your work addresses, and (3) demonstrates familiarity with relevant literature. **Structure:** 1. **Task Context:** What is the broader problem you're solving? Where does it appear in applications? 2. **Category-Based Survey:** Group prior work by approach or technique. This creates a taxonomy that helps readers understand the landscape. 3. **Gap Identification:** For each category, note limitations. Your contribution fills these gaps. 4. **Positioning:** Explain how your approach differs from the closest related work. **Practical Guidelines:** - Read 15-20 papers directly related to your question. Use Semantic Scholar or Google Scholar with citation tracking. - Create a table comparing methods across dimensions relevant to your research question. - Identify the single most related work—your primary baseline. State explicitly why you're building on or diverging from it. **Example Paragraph:** "Mixture-of-expert models (Shazeer et al., 2017) scale parameters without proportionally increasing compute. Subsequent work (Lepikhin et al., 2020) applied this to machine translation, achieving 3x inference speedup through conditional computation. However, these approaches require all experts to be active during training, limiting efficiency. Our work applies expert pruning at training initialization, reducing active parameters by 50% while maintaining convergence rates." **Failure Mode:** The "laundry list" error—describing each paper in isolation without synthesizing common themes or showing the logical progression to your work.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Create a literature table with columns for: Paper, Method, Key Contribution, Limitation. Add a column showing how your work addresses each limitation.