Citation Workflow

# Citation Management & Hallucination Prevention This reference provides a complete workflow for managing citations programmatically, preventing AI-generated citation hallucinations, and maintaining clean bibliographies. --- ## Contents - [Why Citation Verification Matters](#why-citation-verification-matters) - [Citation APIs Overview](#citation-apis-overview) - [Verified Citation Workflow](#verified-citation-workflow) - [Python Implementation](#python-implementation) - [BibTeX Management](#bibtex-management) - [Common Citation Formats](#common-citation-formats) - [Troubleshooting](#troubleshooting) --- ## Why Citation Verification Matters ### The Hallucination Problem Research has documented significant issues with AI-generated citations: - **~40% error rate** in AI-generated citations (Enago Academy research) - NeurIPS 2025 found **100+ hallucinated citations** slipped through review - Common errors include: - Fabricated paper titles with real author names - Wrong publication venues or years - Non-existent papers with plausible metadata - Incorrect DOIs or arXiv IDs ### Consequences - Desk rejection at some venues - Loss of credibility with reviewers - Potential retraction if published - Wasted time chasing non-existent sources ### Solution **Never generate citations from memory—always verify programmatically.** --- ## Citation APIs Overview ### Primary APIs | API | Coverage | Rate Limits | Best For | |-----|----------|-------------|----------| | **Semantic Scholar** | 214M papers | 1 RPS (free key) | ML/AI papers, citation graphs | | **CrossRef** | 140M+ DOIs | Polite pool with mailto | DOI lookup, BibTeX retrieval | | **arXiv** | Preprints | 3-second delays | ML preprints, PDF access | | **OpenAlex** | 240M+ works | 100K/day, 10 RPS | Open alternative to MAG | ### API Selection Guide ``` Need ML paper search? → Semantic Scholar Have DOI, need BibTeX? → CrossRef content negotiation Looking for preprint? → arXiv API Need open data, bulk access? → OpenAlex ``` ### No Official Google Scholar API Google Scholar has no official API. Scraping violates ToS. Use SerpApi ($75-275/month) only if Semantic Scholar coverage is insufficient. --- ## Verified Citation Workflow ### 5-Step Process ``` 1. SEARCH → Query Semantic Scholar with specific keywords ↓ 2. VERIFY → Confirm paper exists in 2+ sources ↓ 3. RETRIEVE → Get BibTeX via DOI content negotiation ↓ 4. VALIDATE → Confirm the claim appears in source ↓ 5. ADD → Add verified entry to .bib file ``` ### Step 1: Search Use Semantic Scholar for ML/AI papers: ```python from semanticscholar import SemanticScholar sch = SemanticScholar() results = sch.search_paper("transformer attention mechanism", limit=10) for paper in results: print(f"Title: {paper.title}") print(f"Year: {paper.year}") print(f"DOI: {paper.externalIds.get('DOI', 'N/A')}") print(f"arXiv: {paper.externalIds.get('ArXiv', 'N/A')}") print(f"Citation count: {paper.citationCount}") print("---") ``` ### Step 2: Verify Existence Confirm paper exists in at least two sources: ```python import requests def verify_paper(doi=None, arxiv_id=None, title=None): """Verify paper exists in multiple sources.""" sources_found = [] # Check Semantic Scholar sch = SemanticScholar() if doi: paper = sch.get_paper(f"DOI:{doi}") if paper: sources_found.append("Semantic Scholar") # Check CrossRef (via DOI) if doi: resp = requests.get(f"https://api.crossref.org/works/{doi}") if resp.status_code == 200: sources_found.append("CrossRef") # Check arXiv if arxiv_id: resp = requests.get( f"http://export.arxiv.org/api/query?id_list={arxiv_id}" ) if "" in resp.text: sources_found.append("arXiv") return len(sources_found) >= 2, sources_found ``` ### Step 3: Retrieve BibTeX Use DOI content negotiation for guaranteed accuracy: ```python import requests def doi_to_bibtex(doi: str) -> str: """Get verified BibTeX from DOI via CrossRef content negotiation.""" response = requests.get( f"https://doi.org/{doi}", headers={"Accept": "application/x-bibtex"}, allow_redirects=True ) response.raise_for_status() return response.text # Example: "Attention Is All You Need" bibtex = doi_to_bibtex("10.48550/arXiv.1706.03762") print(bibtex) ``` ### Step 4: Validate Claims Before citing a paper for a specific claim, verify the claim exists: ```python def get_paper_abstract(doi): """Get abstract to verify claims.""" sch = SemanticScholar() paper = sch.get_paper(f"DOI:{doi}") return paper.abstract if paper else None # Verify claim appears in abstract abstract = get_paper_abstract("10.48550/arXiv.1706.03762") claim = "attention mechanism" if claim.lower() in abstract.lower(): print("Claim appears in paper") ``` ### Step 5: Add to Bibliography Add verified entry to your .bib file with consistent key format: ```python def generate_citation_key(bibtex: str) -> str: """Generate consistent citation key: author_year_firstword.""" import re # Extract author author_match = re.search(r'authors*=s*{([^}]+)}', bibtex, re.I) if author_match: first_author = author_match.group(1).split(',')[0].split()[-1] else: first_author = "unknown" # Extract year year_match = re.search(r'years*=s*{?(d{4})}?', bibtex, re.I) year = year_match.group(1) if year_match else "0000" # Extract title first word title_match = re.search(r'titles*=s*{([^}]+)}', bibtex, re.I) if title_match: first_word = title_match.group(1).split()[0].lower() first_word = re.sub(r'[^a-z]', '', first_word) else: first_word = "paper" return f"{first_author.lower()}_{year}_{first_word}" ``` --- ## Python Implementation ### Complete Citation Manager Class ```python """ Citation Manager - Verified citation workflow for ML papers. """ import requests import time from typing import Optional, List, Dict, Tuple from dataclasses import dataclass try: from semanticscholar import SemanticScholar except ImportError: print("Install: pip install semanticscholar") SemanticScholar = None @dataclass class Paper: title: str authors: List[str] year: int doi: Optional[str] arxiv_id: Optional[str] venue: Optional[str] citation_count: int abstract: Optional[str] class CitationManager: """Manage citations with verification.""" def __init__(self, api_key: Optional[str] = None): self.sch = SemanticScholar(api_key=api_key) if SemanticScholar else None self.verified_papers: Dict[str, Paper] = {} def search(self, query: str, limit: int = 10) -> List[Paper]: """Search for papers using Semantic Scholar.""" if not self.sch: raise RuntimeError("Semantic Scholar not available") results = self.sch.search_paper(query, limit=limit) papers = [] for r in results: paper = Paper( title=r.title, authors=[a.name for a in (r.authors or [])], year=r.year or 0, doi=r.externalIds.get('DOI') if r.externalIds else None, arxiv_id=r.externalIds.get('ArXiv') if r.externalIds else None, venue=r.venue, citation_count=r.citationCount or 0, abstract=r.abstract ) papers.append(paper) return papers def verify(self, paper: Paper) -> Tuple[bool, List[str]]: """Verify paper exists in multiple sources.""" sources = [] # Already found in Semantic Scholar via search sources.append("Semantic Scholar") # Check CrossRef if DOI available if paper.doi: try: resp = requests.get( f"https://api.crossref.org/works/{paper.doi}", timeout=10 ) if resp.status_code == 200: sources.append("CrossRef") except: pass # Check arXiv if ID available if paper.arxiv_id: try: resp = requests.get( f"http://export.arxiv.org/api/query?id_list={paper.arxiv_id}", timeout=10 ) if "" in resp.text and "