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20212026
most citedUser Embedding Model for Personalized Language Prompting

4 citations · 6 across the 9 of their papers we have counts for

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cs.CL2025

REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives

Kun Su, Krishna Sayana, Hubert Pham +8

This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Model…

cs.CL2024

Beyond Retrieval: Generating Narratives in Conversational Recommender Systems

Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski +6

The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectiv…

cs.CL2024

PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting

Liam Hebert, Krishna Sayana, Ambarish Jash +5

Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preference…

cs.CL2024★ 4 cited

User Embedding Model for Personalized Language Prompting

Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash +2

Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recomm…

cs.CL2023

BiPhone: Modeling Inter Language Phonetic Influences in Text

Abhirut Gupta, Ananya B. Sai, Richard Sproat +5

A large number of people are forced to use the Web in a language they have low literacy in due to technology asymmetries. Written text in the second language (L2) from such users o…

cs.CL2023★ 2 cited

Multi-Task End-to-End Training Improves Conversational Recommendation

Naveen Ram, Dima Kuzmin, Ellie Ka In Chio +4

In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a…