Papers by Nikhita Vedula
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context (2026.acl-long)
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Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Mohsen Bayati, Bryan Wang, Shervin Malmasi
| Challenge: | Existing methods for text regression lack local grounding and rely on shared representations. |
| Approach: | They propose a distributional regression model with quantile tokens that insert dedicated quantiles into the input sequence. |
| Outcome: | The proposed method outperforms baseline models on the inside Airbnb and StackSample datasets. |
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers (2024.acl-long)
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Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida, Zhiyu Chen, Giuseppe Castellucci, Nikhita Vedula, Jason Ingyu Choi, Shervin Malmasi
| Challenge: | Large Language Models (LLMs) have given rise to generative recommenders . however, improving the generated content through user feedback is prohibitively expensive . |
| Approach: | They propose a generative explore-exploit method that exploits items with high engagement and actively explores hidden population preferences to improve recommendation quality. |
| Outcome: | The proposed approach exploits items with high engagement and actively explores hidden population preferences to improve recommendation quality. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching (2025.acl-long)
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Xiangci Li, Zhiyu Chen, Jason Ingyu Choi, Nikhita Vedula, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi
| Challenge: | Prior human-annotated CPS datasets are small in size and lack integration with real-world product search systems. |
| Approach: | They propose a method to generate target-oriented shopping conversations without human annotations by using large language models. |
| Outcome: | The proposed method achieves highly natural and coherent conversations from three shopping domains and significantly improves on human evaluations and downstream tasks. |
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings (2022.coling-1)
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Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
| Challenge: | Existing Conversational Task Assistants fail to provide a comprehensive natural conversation that includes search, context-aware QA, step-by-step instructions. |
| Approach: | They present a corpus of conversations in two domains: cooking and home improvement . they crowd-sourced 549 conversations with an asynchronous Wizard-of-Oz setup . |
| Outcome: | The proposed model performs well in both Intent Classification and Abstractive Question Answering tasks, but the performance is poor on AQA tasks. |
Generative Product Recommendations for Implicit Superlative Queries (2025.naacl-srw)
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| Challenge: | Existing retrieval and ranking systems struggle with implicit superlative queries . lack of explicit attribute mentions and complexity of the query complicates ranking . |
| Approach: | They propose a four-point schema for annotating the best product candidates for superlative queries . they propose pointwise, deliberated pointwise and pairwise methods to analyze the results . |
| Outcome: | The proposed schema can be used to rank products with implicit attributes and reason over them. |
Fact Checking Machine Generated Text with Dependency Trees (2022.emnlp-industry)
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| Challenge: | Recent work has noted the benefits of natural language text generated by NLG systems over fixed templates. |
| Approach: | They propose a method that checks factuality of input text based on structured knowledge patterns and dependency relations with respect to the input text. |
| Outcome: | The proposed technique outperforms state-of-the-art techniques in this special, but important case. |
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction (2026.findings-acl)
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| Challenge: | Existing methods for text generation require multiple independent sequences to be decoded in parallel. |
| Approach: | They propose an algorithm that accelerates offline decoding by leveraging shared memory and computation across batches. |
| Outcome: | Experiments show that attribute-value pairs are conditionally independent, enabling decoding in parallel up to 96 tokens per prompt. |
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. |