Document Ranking

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

7 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 65 papers with code (168 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• XLNet: Generalized Autoregressive Pretraining for Language Understanding
  19 Jun 2019 27 repositories listed Syntology ran 10 of 24 samples · 14 unverified · 3 pointer-only (licence)
  With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
  27 Apr 2020 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
• CEDR: Contextualized Embeddings for Document Ranking
  15 Apr 2019 7 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
• Learning deep structured semantic models for web search using clickthrough data
  27 Oct 2013 6 repositories listed
  The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data.
• Context Attentive Document Ranking and Query Suggestion
  5 Jun 2019 5 repositories listed
  We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance.
• The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
  14 Jan 2021 4 repositories listed
  We propose a design pattern for tackling text ranking problems, dubbed "Expando-Mono-Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different domains.
• Simplified TinyBERT: Knowledge Distillation for Document Retrieval
  16 Sep 2020 4 repositories listed
  Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.
• Neural Vector Spaces for Unsupervised Information Retrieval
  9 Aug 2017 4 repositories listed
  We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.
• Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding
  4 Jul 2022 3 repositories listed
  We found direct evidence of this bias in some test sets, which motivated us to create MS MARCO FarRelevant (based on MS MARCO Passages) where the relevant passages were not present among the first 512 tokens.
• Multi-Stage Document Ranking with BERT
  31 Oct 2019 3 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing.
• IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
  30 May 2017 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval…
• CODEC: Complex Document and Entity Collection
  9 May 2022 2 repositories listed
  We also show that the manual query reformulations significantly improve document ranking and entity ranking performance.
• Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits
  12 Feb 2021 2 repositories listed
  We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end.
• Traditional IR rivals neural models on the MS MARCO Document Ranking Leaderboard
  15 Dec 2020 2 repositories listed
  This short document describes a traditional IR system that achieved MRR@100 equal to 0.
• Document Ranking with a Pretrained Sequence-to-Sequence Model
  14 Mar 2020 2 repositories listed
  We investigate this observation further by varying target words to probe the model's use of latent knowledge.
• Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
  12 Jun 2025 1 repository listed
  In this paper, we aim to improve the effectiveness of pointwise methods while preserving their efficiency through two key innovations: (1) We propose a novel Global-Consistent Comparative Pointwise Ranking (GCCP)…
• How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
  10 Apr 2025 1 repository listed
  Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment generation.
• Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
  4 Apr 2025 1 repository listed
  We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimization.
• Beyond Questions: Leveraging ColBERT for Keyphrase Search
  4 Dec 2024 1 repository listed
  Then, using those keyphrases, we train a keyphrase-based ColBERT ranker (ColBERTKP_QD) to improve the performance when working with keyphrase queries.
• DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
  10 Oct 2024 1 repository listed
  Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments.
• RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation
  9 Jun 2024 1 repository listed
  The Retrieval Augmented Generation (RAG) framework utilizes a combination of parametric knowledge and external knowledge to demonstrate state-of-the-art performance on open-domain question answering tasks.
• PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
  29 Apr 2024 1 repository listed Syntology ran 6 of 6 samples · 0 unverified
  Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: (1) prompt-based re-ranking methods, which require no further training but are only feasible for re-ranking a handful of…
• RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
  27 Mar 2024 1 repository listed
  In this work, we examine \mamba's efficacy through the lens of a classical IR task -- document ranking.
• Query Augmentation by Decoding Semantics from Brain Signals
  24 Feb 2024 1 repository listed Syntology ran 12 of 18 samples · 6 unverified
  If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well.
• Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
  19 Feb 2024 1 repository listed
  In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system's usefulness and trustworthiness for downstream users.
• Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
  20 Oct 2023 1 repository listed
  In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document.
• A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
  14 Oct 2023 1 repository listed
  We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach.
• Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
  24 May 2023 1 repository listed
  Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step.
• Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
  27 Feb 2023 1 repository listed
  Pre-trained language models have achieved great success in various large-scale information retrieval tasks.
• LEAD: Liberal Feature-based Distillation for Dense Retrieval
  10 Dec 2022 1 repository listed
  Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.