research paper
**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