13 citations · 27 across the 17 of their papers we have counts for
18 papers
Scaling E-Commerce Attribute Extraction with Parallel Decoding
Nikhita Vedula, Dushyanta Dhyani, Bryan Wang +1
Customers rely on specific product attributes to compare products and make purchasing decisions, but e-commerce catalogs are messy and unstructured, making it difficult to identify…
Intra-Prompt Parallel Decoding for Common-Context Question Answering
Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani +3
In common-context question answering (CCQA) tasks, multiple input questions share a common context to base their answers from. However, Large Language Models typically generate eac…
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…
Generative Product Recommendations for Implicit Superlative Queries
Kaustubh D. Dhole, Nikhita Vedula, Saar Kuzi +3
In Recommender Systems, users often seek the best products through indirect, vague, or under-specified queries, such as "best shoes for trail running". Such queries, also referred…
Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching
Xiangci Li, Zhiyu Chen, Jason Ingyu Choi +4
The goal of conversational product search (CPS) is to develop an intelligent, chat-based shopping assistant that can directly interact with customers to understand shopping intents…
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers
Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida +5
Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate ite…
Question Suggestion for Conversational Shopping Assistants Using Product Metadata
Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi
Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generat…
Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces
Nikhita Vedula, Giuseppe Castellucci, Eugene Agichtein +2
Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting,…
Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulation
Besnik Fetahu, Tejas Mehta, Qun Song +3
E-commerce customers frequently seek detailed product information for purchase decisions, commonly contacting sellers directly with extended queries. This manual response requireme…
Faithful Low-Resource Data-to-Text Generation through Cycle Training
Zhuoer Wang, Marcus Collins, Nikhita Vedula +3
Methods to generate text from structured data have advanced significantly in recent years, primarily due to fine-tuning of pre-trained language models on large datasets. However, s…
Automatic Discovery of Novel Intents & Domains from Text Utterances
Nikhita Vedula, Rahul Gupta, Aman Alok +1
One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing rese…
Towards Open Intent Discovery for Conversational Text
Nikhita Vedula, Nedim Lipka, Pranav Maneriker +1
Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model…
Multimodal Content Analysis for Effective Advertisements on YouTube
Nikhita Vedula, Wei Sun, Hyunhwan Lee +5
The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a mult…