# Overview of the TREC 2022 deep learning track

arXiv: https://arxiv.org/abs/2507.10865 · HTML: https://mlsanity.com/paper/2507.10865/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime](https://mlsanity.com/paper/2104.09399/) (2021)
- [Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review](https://mlsanity.com/paper/1504.06868/) (2015)
- [Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard](https://mlsanity.com/paper/2102.12887/) (2021)
- [Optimizing Query Evaluations using Reinforcement Learning for Web Search](https://mlsanity.com/paper/1804.04410/) (2018)
- [Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks](https://mlsanity.com/paper/1907.03693/) (2019)
- [On Survivorship Bias in MS MARCO](https://mlsanity.com/paper/2204.12852/) (2022)

## Built on by (most cited first, 22 of 22)

- [Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain](https://mlsanity.com/paper/2509.03787/) (2025)
- [An Efficient Rubric-based Generative Verifier for Search-Augmented LLMs](https://mlsanity.com/paper/2510.14660/) (2025)
- [Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration](https://mlsanity.com/paper/2604.10150/) (2026)
- [Test-time Corpus Feedback: From Retrieval to RAG](https://mlsanity.com/paper/2508.15437/) (2025)
- [Rethinking On-policy Optimization for Query Augmentation](https://mlsanity.com/paper/2510.17139/) (2025)
- [RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution](https://mlsanity.com/paper/2602.16932/) (2026)
- [100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models](https://mlsanity.com/paper/2603.15970/) (2026)
- [Learning When to Attend: Conditional Memory Access for Long-Context LLMs](https://mlsanity.com/paper/2603.17484/) (2026)
- [Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing](https://mlsanity.com/paper/2512.00390/) (2025)
- [Understanding Matching Mechanisms in Cross-Encoders](https://mlsanity.com/paper/2507.14604/) (2025)
- [Lighting the Way for BRIGHT: Reproducible Baselines with Anserini, Pyserini, and RankLLM](https://mlsanity.com/paper/2509.02558/) (2025)
- [V3DB: Audit-on-Demand Zero-Knowledge Proofs for Verifiable Vector Search over Committed Snapshots](https://mlsanity.com/paper/2603.03065/) (2026)
- [Still Fresh? Evaluating Temporal Drift in Retrieval Benchmarks](https://mlsanity.com/paper/2603.04532/) (2026)
- [AgentSearchBench: A Benchmark for AI Agent Search in the Wild](https://mlsanity.com/paper/2604.22436/) (2026)
- [Can LLM Rerankers Predict Their Own Ranking Performance?](https://mlsanity.com/paper/2606.03535/) (2026)
- [RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback](https://mlsanity.com/paper/2510.04096/) (2025)
- [Re-Rankers as Relevance Judges](https://mlsanity.com/paper/2601.04455/) (2026)
- [BlitzRank: Principled Zero-shot Ranking Agents with Tournament Graphs](https://mlsanity.com/paper/2602.05448/) (2026)
- [RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation](https://mlsanity.com/paper/2604.27244/) (2026)
- [Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps](https://mlsanity.com/paper/2607.00004/) (2026)
- [Structure-aware Relative Policy Optimization for Ranking](https://mlsanity.com/paper/2607.25268/) (2026)
- [VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models](https://mlsanity.com/paper/2609.01325/) (2026)

## Abstract (arXiv)

This is the fourth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels available for both passage and document ranking tasks. In addition, this year we also leverage both the refreshed passage and document collections that were released last year leading to a nearly 16 times increase in the size of the passage collection and nearly four times increase in the document collection size. Unlike previous years, in 2022 we mainly focused on constructing a more complete test collection for the passage retrieval task, which has been the primary focus of the track. The document ranking task was kept as a secondary task, where document-level labels were inferred from the passage-level labels. Our analysis shows that similar to previous years, deep neural ranking models that employ large scale pretraining continued to outperform traditional retrieval methods. Due to the focusing our judging resources on passage judging, we are more confident in the quality of this year's queries and judgments, with respect to our ability to distinguish between runs and reuse the dataset in future. We also see some surprises in overall outcomes. Some top-performing runs did not do dense retrieval. Runs that did single-stage dense retrieval were not as competitive this year as they were last year.