CODEC: Complex Document and Entity Collection
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Abstract (arXiv)
CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks?". CODEC includes 42 topics developed by researchers and a new focused web corpus with semantic annotations including entity links. This resource includes expert judgments on 17,509 documents and entities (416.9 per topic) from diverse automatic and interactive manual runs. The manual runs include 387 query reformulations, providing data for query performance prediction and automatic rewriting evaluation. CODEC includes analysis of state-of-the-art systems, including dense retrieval and neural re-ranking. The results show the topics are challenging with headroom for document and entity ranking improvement. Query expansion with entity information shows significant gains in document ranking, demonstrating the resource's value for evaluating and improving entity-oriented search. We also show that the manual query reformulations significantly improve document ranking and entity ranking performance. Overall, CODEC provides challenging research topics to support the development and evaluation of entity-centric search methods.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 May 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 2; 2026: 1.
7 papers.
- 2024INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuningcited by 19
- 2024DREQ: Document Re-Ranking Using Entity-based Query Understandingcited by 5
- 2022Query-Specific Knowledge Graphs for Complex Finance Topicscited by 1
- 2025QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Rankingcited by 1
- 2026Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Rankingcited by 1
- 2024Understanding the User: An Intent-Based Ranking Datasetcited by 0
- 2025REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Rankingcited by 0
- 2026Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking14 Jun 2026
- 2025REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking13 Oct 2025
- 2025QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking13 Oct 2025
- 2024Understanding the User: An Intent-Based Ranking Dataset30 Aug 2024
- 2024INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning12 Jan 2024
- 2024DREQ: Document Re-Ranking Using Entity-based Query Understanding11 Jan 2024
- 2022Query-Specific Knowledge Graphs for Complex Finance Topics8 Nov 2022