retrieval-augmented generation for knowledge-intensive nlp tasks — search2

Evidence analysis for retrieval-augmented generation for knowledge-intensive nlp tasks. Supporting and contradicting evidence presented side-by-side with full provenance from independent sources.

Data Sources

Results synthesized from web crawls, academic citations (Semantic Scholar), patent filings (USPTO), corporate ownership records (SEC EDGAR, Companies House), software dependency graphs (npm, PyPI, crates.io), infrastructure records (DNS, TLS certificates, IP ranges), temporal archives (Wayback Machine), government datasets (Data.gov), and conversational sources (Hacker News, Reddit).

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