Navigating AI Research Papers
Where to Find Papers
Primary Sources
| Source | Content | Access |
|---|---|---|
| arXiv | Preprints, AI/ML/CS | Free, daily updates |
| OpenReview | Peer-reviewed (ICLR, NeurIPS) | Free, with reviews |
| ACL Anthology | NLP papers | Free |
| Semantic Scholar | Aggregated, citations | Free, great search |
| Google Scholar | Universal academic search | Free |
Key Conferences
| Conference | Focus | When |
|---|---|---|
| NeurIPS | ML general | December |
| ICML | ML general | July |
| ICLR | Deep learning | May |
| ACL/EMNLP | NLP | Various |
| CVPR/ICCV | Computer vision | Various |
Reading Research Papers Efficiently
Paper Sections
| Section | What to Look For | Time |
|---|---|---|
| Abstract | Problem, method, results | 2 min |
| Introduction | Motivation, contributions | 5 min |
| Related Work | Context and positioning | Skim |
| Method | Technical details | Focus |
| Experiments | Benchmarks, ablations | Focus |
| Conclusion | Summary, limitations | 2 min |
Three-Pass Reading 1. Pass 1 (5 min): Title, abstract, figures, conclusion 2. Pass 2 (30 min): Introduction, methods overview, results 3. Pass 3 (1+ hour): Full technical details, reproduce
Summarizing Papers with LLMs
Prompt Template
Summarize this paper in the following format:
1. **Problem**: What problem does this paper address?
2. **Key Insight**: What is the core contribution?
3. **Method**: How does it work (high level)?
4. **Results**: What are the main findings?
5. **Limitations**: What are the known limitations?
6. **Relevance**: Why might this matter for practitioners?
Staying Current
- Subscribe to arXiv daily digests (cs.LG, cs.CL)
- Follow researchers on Twitter/X
- Join paper reading groups
- Use tools like Papers With Code, Daily Papers
- Review conference accepted papers annually
Critical Reading Skills
- Distinguish hype from genuine contribution
- Check statistical significance and error bars
- Note dataset/benchmark limitations
- Consider computational requirements
- Look for code/reproducibility
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