Benchmark for Complex Answer Retrieval
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Abstract (arXiv)
Retrieving paragraphs to populate a Wikipedia article is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a comprehensive dataset that targets this retrieval scenario. We present early results from a variety of approaches -- from standard information retrieval methods (e.g., tf-idf) to complex systems that using query expansion using knowledge bases and deep neural networks. The goal is to offer future participants of this track an overview of some promising approaches to tackle this problem.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 May 2017.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2025DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesiscited by 34
- 2021MS MARCO: Benchmarking Ranking Models in the Large-Data Regimecited by 28
- 2021Self-supervised Answer Retrieval on Clinical Notescited by 2
- 2021Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independencecited by 1
- 2026Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coveragecited by 1
- 2021Quality and Cost Trade-offs in Passage Re-ranking Taskcited by 0
- 2026Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage9 Mar 2026
- 2025DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis27 Aug 2025
- 2021Quality and Cost Trade-offs in Passage Re-ranking Task18 Nov 2021
- 2021Self-supervised Answer Retrieval on Clinical Notes2 Aug 2021
- 2021MS MARCO: Benchmarking Ranking Models in the Large-Data Regime9 May 2021
- 2021Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence19 Apr 2021