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Anserini Regressions: TREC 2025 RAG Track Test Topics

Models: various bag-of-words approaches on segmented documents

This page describes regression experiments for ranking on the segmented version of the MS MARCO V2.1 document corpus using the RAG 25 test topics (= narratives), which is integrated into Anserini's regression testing framework. This corpus was derived from the MS MARCO V2 segmented document corpus and re-used for the TREC 2025 RAG Track. Instructions for downloading the corpus can be found here.

Evaluation uses qrels over 22 topics from the TREC 2025 RAG Track test set. These qrels represent manual relevance judgments from NIST assessors, contrasted with automatically generated UMBRELA judgments. More details can be found in the following paper:

Shivani Upadhyay, Nandan Thakur, Ronak Pradeep, Nick Craswell, Daniel Campos and Jimmy Lin. Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track. arXiv:2603.09891, March 2026.

Here, we cover bag-of-words baselines where each segment in the MS MARCO V2.1 segmented document corpus is treated as a unit of indexing.

The exact configurations for these regressions are stored in this YAML file. Note that this page is automatically generated from this template as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead.

From one of our Waterloo servers (e.g., orca), the following command will perform the complete regression, end to end:

bin/run.sh io.anserini.reproduce.ReproduceFromDocumentCollection --index --verify --search --config rag25-doc-segmented-test-umbrela2

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -threads 24 \
  -collection MsMarcoV2DocCollection \
  -input /path/to/msmarco-v2.1-doc-segmented \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-inverted.msmarco-v2.1-doc-segmented/ \
  -storeRaw \
  >& logs/log.msmarco-v2.1-doc-segmented &

The setting of -input should be a directory containing the compressed jsonl files that comprise the corpus.

For additional details, see explanation of common indexing options.

Retrieval

Here, we are using 22 test topics from the TREC 2025 RAG Track with manual relevance judgments from NIST assessors. Topics and qrels are stored here, which is linked to the Anserini repo as a submodule.

After indexing has completed, you should be able to perform retrieval as follows:

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2.1-doc-segmented/ \
  -topics tools/topics-and-qrels/topics.rag25.test.jsonl \
  -topicReader JsonString \
  -output runs/run.msmarco-v2.1-doc-segmented.bm25-default.topics.rag25.test.jsonl.txt \
  -bm25 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2.1-doc-segmented/ \
  -topics tools/topics-and-qrels/topics.rag25.test.jsonl \
  -topicReader JsonString \
  -output runs/run.msmarco-v2.1-doc-segmented.bm25-default+rm3.topics.rag25.test.jsonl.txt \
  -bm25 -rm3 -collection MsMarcoV2DocCollection &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2.1-doc-segmented/ \
  -topics tools/topics-and-qrels/topics.rag25.test.jsonl \
  -topicReader JsonString \
  -output runs/run.msmarco-v2.1-doc-segmented.bm25-default+rocchio.topics.rag25.test.jsonl.txt \
  -bm25 -rocchio -collection MsMarcoV2DocCollection &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -m ndcg_cut.30 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default.topics.rag25.test.jsonl.txt
bin/trec_eval -c -m ndcg_cut.100 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default.topics.rag25.test.jsonl.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default.topics.rag25.test.jsonl.txt

bin/trec_eval -c -m ndcg_cut.30 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default+rm3.topics.rag25.test.jsonl.txt
bin/trec_eval -c -m ndcg_cut.100 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default+rm3.topics.rag25.test.jsonl.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default+rm3.topics.rag25.test.jsonl.txt

bin/trec_eval -c -m ndcg_cut.30 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default+rocchio.topics.rag25.test.jsonl.txt
bin/trec_eval -c -m ndcg_cut.100 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default+rocchio.topics.rag25.test.jsonl.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.rag25.test-umbrela2.txt runs/run.msmarco-v2.1-doc-segmented.bm25-default+rocchio.topics.rag25.test.jsonl.txt

Effectiveness

With the above commands, you should be able to reproduce the following results:

nDCG@30 BM25 (default) +RM3 +Rocchio
RAG 25: Test queries 0.3250 0.2736 0.3306
nDCG@100 BM25 (default) +RM3 +Rocchio
RAG 25: Test queries 0.2835 0.2345 0.2767
R@100 BM25 (default) +RM3 +Rocchio
RAG 25: Test queries 0.1167 0.0909 0.1092