| name | keyword-extraction |
| description | Guidelines for extracting arXiv search keywords from fuzzy research ideas. Use before ExtractKeywords or when EvaluatePapers suggests retry. Ensures keywords are technical, domain-specific, and suitable for literature retrieval. |
Keyword Extraction for arXiv
Guidelines for turning fuzzy research ideas into effective arXiv search keywords.
Principles
- Technical over generic: Prefer domain nouns (e.g. "transformer", "reinforcement learning") over vague terms ("research", "study").
- 3–5 keywords: Enough to narrow scope; avoid single overly broad or many overlapping terms.
- No stop words: Exclude "the", "a", "and", "for", "with" — arXiv handles these.
- Combine concepts: If idea spans multiple areas, include 1–2 terms per area (e.g. "federated learning" + "privacy" + "healthcare").
- Use arXiv categories when inferable: cs.AI, cs.LG, stat.ML for ML; cs.CL for NLP; etc.
Common Patterns
| Idea type | Keyword strategy |
|---|
| Method + domain | Method term + application domain (e.g. "attention mechanism", "image segmentation") |
| Comparison | Both compared items + comparison dimension (e.g. "BERT", "GPT", "benchmark") |
| Gap analysis | Broad area + "survey" or "review" + time constraint ("2020", "recent") |
| Implementation | Framework/tool + task (e.g. "PyTorch", "fine-tuning") |
Retry Triggers
If EvaluatePapers returns score < 3, consider:
- Narrowing keywords (add domain or constraint)
- Replacing generic terms with technical synonyms
- Splitting one broad idea into 2–3 focused keyword sets and searching separately
Examples
- "用深度学习做医学图像分析" → ["deep learning", "medical image", "diagnosis", "CNN"]
- "大模型在代码生成上的应用" → ["large language model", "code generation", "programming"]
- "联邦学习隐私保护" → ["federated learning", "privacy", "differential privacy"]
