Document Ranking
56 papers with code • 2 benchmarks • 6 datasets
Sort documents according to some criterion so that the "best" results appear early in the result list displayed to the user (Source: Wikipedia).
Libraries
Use these libraries to find Document Ranking models and implementationsLatest papers
Query Augmentation by Decoding Semantics from Brain Signals
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 Large Language Models
The process of scale calibration in ranking systems involves adjusting the outputs of rankers to correspond with significant qualities like click-through rates or relevance, crucial for mirroring real-world value and thereby boosting the system's effectiveness and reliability.
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
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
Our approach reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, significantly improving the efficiency of LLM-based zero-shot ranking.
Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
Pre-trained language models have achieved great success in various large-scale information retrieval tasks.
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model.
Principled Multi-Aspect Evaluation Measures of Rankings
Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query.
Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query.
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.
Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding
Most other models had poor zero-shot performance (sometimes at a random baseline level) but outstripped MaxP by as much 13-28\% after finetuning.