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a5b5502 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Baselines
The result tables keep all baseline families used during development: TF-IDF, BM25, MiniLM/FAISS, Contriever, SPLADE++, BGE, ColBERT, and the historical version of OURS.
`run_local_lexical.py` reproduces local TF-IDF and an effectiveness-oriented BM25 sanity check. Its BM25 latency is **not** a production inverted-index benchmark and is labelled accordingly.
For neural/vector baselines, use the authors' released models and index libraries. Their effectiveness values in `results/beir/rag_top10_pool_sweep_scifact_treccovid.json` are retained as the comparison ledger from the experiment campaign. Before a publication-quality speed claim, rerun every baseline on the same CPU and report both:
- query encoder time;
- index/search time;
- end-to-end query time.
Do not compare an end-to-end sparse retrieval number with ANN-only latency that excludes query embedding inference.
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