11 papers
From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
Yilun Zhu, Nikhita Vedula, Shervin Malmasi
Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies across categories and contexts. Tra…
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction
Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani +2
Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context. While standard autoregressive…
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context
Yilun Zhu, Yuan Zhuang, Nikhita Vedula +6
Many applications of LLM-based text regression require predicting a full conditional distribution rather than a single point value. We study distributional regression under empiric…
A Modular LLM Framework for Explainable Price Outlier Detection
Shadi Sartipi, John Wu, Sina Ghotbi +2
Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trus…
Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition
Yilun Zhu, Nikhita Vedula, Shervin Malmasi
Search queries with superlatives (e.g., best, most popular) require comparing candidates across multiple dimensions, demanding linguistic understanding and domain knowledge. We sho…
Quantile Regression with Large Language Models for Price Prediction
Nikhita Vedula, Dushyanta Dhyani, Laleh Jalali +3
Large Language Models (LLMs) have shown promise in structured prediction tasks, including regression, but existing approaches primarily focus on point estimates and lack systematic…