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Found 12 papers, 5 papers with code
Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers
We first show that directly applying the expansion techniques in the current literature to state-of-the-art neural rankers can result in deteriorated zero-shot performance.
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem.
Query Expansion by Prompting Large Language Models
Query expansion is a widely used technique to improve the recall of search systems.
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design.
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT.
Accelerated Convergence for Counterfactual Learning to Rank
Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system.
Safe Exploration for Optimizing Contextual Bandits
Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.
Unbiased Learning to Rank: Counterfactual and Online Approaches
Through randomization the effect of different types of bias can be removed from the learning process.
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
List-wise learning to rank methods are considered to be the state-of-the-art.
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery.
A Directional Diffusion Algorithm for Inpainting
The problem of inpainting involves reconstructing the missing areas of an image.
