2nd/10_Wiki/Topics/Other/Related-Work.md

---
id: wiki-2026-0508-related-work
title: Related Work
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Related Work Section, Prior Art, Literature Review]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [research, academic-writing, papers]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: english-korean
  framework: research-writing
---

# Related Work

## 매 한 줄
> **"매 academic paper 의 mandatory section 의 prior literature 의 position 의 own contribution"**. ML/CS papers (NeurIPS, ICML, ICLR, ACL, CVPR) 의 standard structure 의 Introduction → Related Work → Method → Experiments → Conclusion. 매 reviewer 의 first read — 매 novelty claim 의 here 의 stand or fall.

## 매 핵심

### 매 Purpose
1. **Position** — 매 own work 의 landscape 의 place.
2. **Differentiate** — 매 prior 의 limitation 의 explicit, 매 own gap 의 fill.
3. **Credit** — 매 intellectual lineage 의 acknowledge.
4. **Scope** — 매 reviewer 의 not-cited prior work 의 reject 의 prevent.

### 매 Structure (typical)
- **Thematic grouping** (preferred 2026) — 매 theme 의 paragraph 의 each.
- *Chronological* — only for survey papers.
- *Per-paper* — verbose, avoid.

### 매 Typical Categories (ML paper)
1. Foundation / closest direct prior.
2. Methodology family (e.g., diffusion vs flow-matching).
3. Application domain.
4. Concurrent work (last 6 months).

### 매 응용
1. Conference paper — 매 0.5-1 page Related Work section.
2. Thesis — 매 standalone chapter (10-30 pages).
3. Grant proposal — 매 "Innovation" section 의 backbone.
4. Patent — 매 "Background of the Invention".

## 💻 패턴

### LaTeX section template
```latex
\section{Related Work}

\paragraph{Foundation models for X.}
\citet{vaswani2017} introduced the Transformer, which subsequent work
\citep{devlin2019,brown2020,touvron2023llama} scaled to billions of parameters.
Unlike these, our method targets edge inference (\textsection\ref{sec:method}).

\paragraph{Efficient inference.}
Quantization \citep{dettmers2022int8,frantar2023gptq} and speculative decoding
\citep{leviathan2023speculative,chen2023accelerating} reduce latency, but
neither addresses our setting of dynamic batch size.

\paragraph{Concurrent work.}
\citet{smith2026concurrent} appeared on arXiv in March 2026; we differ in
that we additionally support streaming output.
```

### BibTeX management (`.bib`)
```bibtex
@inproceedings{vaswani2017,
  title={Attention is all you need},
  author={Vaswani, Ashish and others},
  booktitle={NeurIPS},
  year={2017}
}

@article{brown2020,
  title={Language Models are Few-Shot Learners},
  author={Brown, Tom and others},
  journal={NeurIPS},
  year={2020}
}
```

### Paper graph extraction (Python `semanticscholar`)
```python
from semanticscholar import SemanticScholar
sch = SemanticScholar()

paper = sch.get_paper("10.48550/arXiv.1706.03762")  # Attention is all you need
for ref in paper.references[:10]:
    print(ref.title, "→", ref.year)

# Forward citations
cites = sch.get_paper_citations(paper.paperId, limit=50)
```

### Related work table (Markdown)
```markdown
| Method | Modality | Latency | Param-free | Ours |
|---|---|---|---|---|
| GPTQ | LLM | medium | no | -- |
| AWQ | LLM | low | no | -- |
| FlashAttn | LLM | low | yes | similar |
| **Ours** | **LLM+Vision** | **lowest** | **yes** | -- |
```

### LLM-assisted citation finder (Anthropic SDK)
```python
import anthropic
client = anthropic.Anthropic()

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=600,
    tools=[{"type": "web_search_20250305", "name": "web_search"}],
    messages=[{
        "role": "user",
        "content": "Find 5 ICLR/NeurIPS 2024-2026 papers on speculative decoding with multi-token prediction. Return BibTeX."
    }]
)
print(resp.content[0].text)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Conference paper | Thematic, 0.5-1 page |
| Survey paper | Chronological + thematic |
| Thesis | Dedicated chapter |
| Industry blog | "Inspired by" + light citation |
| Patent | "Background" with prior art table |

**기본값**: thematic grouping, 매 group 당 3-5 cites, concurrent work 의 explicit paragraph.

## 🔗 Graph
- 부모: [[Academic-Writing]] · [[Research-Methodology]]
- 변형: [[Literature-Review]]

## 🤖 LLM 활용
**언제**: paper search 의 expand, group prior work 의 thematically, 매 differentiation paragraph 의 draft.
**언제 X**: 매 hallucinated citation 의 risk — 매 always verify 의 DOI / arXiv ID.

## ❌ 안티패턴
- **Citation dump (no commentary)**: "[Smith 2020, Jones 2021, Lee 2022] also did X." — 매 reader 의 differentiation 의 unclear.
- **"To the best of our knowledge"**: 매 cliché — 매 specific 의 prefer.
- **Concurrent work 의 ignore**: 매 reviewer 의 catch — proactive 의 cite.
- **Hallucinated citations**: 매 LLM-generated 매 always 의 verify.
- **Self-citation 의 over-rely**: 매 inflate own lineage.

## 🧪 검증 / 중복
- Verified (NeurIPS/ICML author guidelines, Goodson "How to Write Related Work" 2024).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — thematic structure + LaTeX template + comparison table |