Papers by Boris N. Oreshkin

1 papers
Quantile Regression with Large Language Models for Price Prediction (2025.findings-acl)

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Challenge: Existing approaches to structured prediction tasks focus on point estimates and lack systematic comparison across different methods.
Approach: They propose a novel quantile regression approach that enables LLMs to produce full predictive distributions, improving upon traditional point estimates.
Outcome: The proposed model outperforms encoder architectures, embedding-based methods, and few-shot learning methods in prediction accuracy and distributional calibration.

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