Document Ranking
57 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).
Benchmarks
These leaderboards are used to track progress in Document Ranking
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Libraries
Use these libraries to find Document Ranking models and implementations
CLUE
Qulac
Latest papers with no code
The Surprising Effectiveness of Rankers Trained on Expanded Queries
In our extensive experiments on the DL-Hard dataset, we find that a principled query performance based scoring method using base and specialized ranker offers a significant improvement of up to 25% on the passage ranking task and up to 48. 4% on the document ranking task when compared to the baseline performance of using original queries, even outperforming SOTA model.
High Recall, Small Data: The Challenges of Within-System Evaluation in a Live Legal Search System
We show these challenges with log data from a live legal search system and two user studies.
Measuring Bias in a Ranked List using Term-based Representations
With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.
Recency Ranking by Diversification of Result Set
In this paper, we propose a web search retrieval approach which automatically detects recency sensitive queries and increases the freshness of the ordinary document ranking by a degree proportional to the probability of the need in recent content.
Data Augmentation for Sample Efficient and Robust Document Ranking
We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model.
Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative information retrieval systems.
Personalized Search Via Neural Contextual Semantic Relevance Ranking
Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference.
Context Aware Query Rewriting for Text Rankers using LLM
We find that there are two inherent limitations of using LLMs as query re-writers -- concept drift when using only queries as prompts and large inference costs during query processing.
Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track
Large-scale text retrieval technology has been widely used in various practical business scenarios.
GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion.
