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Found 12 papers, 1 papers with code
Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results.
Stream-Flow Forecasting of Small Rivers Based on LSTM
Stream-flow forecasting for small rivers has always been of great importance, yet comparatively challenging due to the special features of rivers with smaller volume.
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees
We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.
Improving Neural Ranking via Lossless Knowledge Distillation
We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized identically to their teachers.
Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank
We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.
Rank4Class: A Ranking Formulation for Multiclass Classification
Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes.
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.
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.
Learning to Rank when Grades Matter
In this paper, we conduct a rigorous study of learning to rank with grades, where both ranking performance and grade prediction performance are important.
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.
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
We propose to incorporate fine-grained relevance labels into the prompt for LLM rankers, enabling them to better differentiate among documents with different levels of relevance to the query and thus derive a more accurate ranking.
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing
Our method takes both LLM generated relevance labels and pairwise preferences.
