105 citations · 337 across the 46 of their papers we have counts for
45 papers · 1 filter
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…
Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
Kalle Kujanpää, Ning Liu, Shahnawaz Alam +4
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an ag…
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…
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…
Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce
Saar Kuzi, Shervin Malmasi
Consumers on a shopping mission often leverage both product search and information seeking systems, such as web search engines and Question Answering (QA) systems, in an iterative…
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,…
Identifying Shopping Intent in Product QA for Proactive Recommendations
Besnik Fetahu, Nachshon Cohen, Elad Haramaty +3
Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question…
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data
Parth Patwa, Simone Filice, Zhiyu Chen +3
Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. In-Context Learning (ICL) typically achieves better…
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…
Controllable Decontextualization of Yes/No Question and Answers into Factual Statements
Lingbo Mo, Besnik Fetahu, Oleg Rokhlenko +1
Yes/No or polar questions represent one of the main linguistic question categories. They consist of a main interrogative clause, for which the answer is binary (assertion or negati…
Follow-on Question Suggestion via Voice Hints for Voice Assistants
Besnik Fetahu, Pedro Faustini, Giuseppe Castellucci +3
The adoption of voice assistants like Alexa or Siri has grown rapidly, allowing users to instantly access information via voice search. Query suggestion is a standard feature of sc…
InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko +1
E-commerce product catalogs contain billions of items. Most products have lengthy titles, as sellers pack them with product attributes to improve retrieval, and highlight key produ…
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
Besnik Fetahu, Zhiyu Chen, Sudipta Kar +2
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This da…
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
Zhiyu Chen, Jason Choi, Besnik Fetahu +2
Customers interacting with product search engines are increasingly formulating information-seeking queries. Frequently Asked Question (FAQ) retrieval aims to retrieve common questi…
Answering Unanswered Questions through Semantic Reformulations in Spoken QA
Pedro Faustini, Zhiyu Chen, Besnik Fetahu +2
Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencie…
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
Besnik Fetahu, Sudipta Kar, Zhiyu Chen +2
We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify…
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…