4 citations · 4 across the 7 of their papers we have counts for
8 papers
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Yilun Hao, Krishna Sayana, Isabella Ye +4
Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed simil…
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…
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…
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
Liam Hebert, Marialena Kyriakidi, Hubert Pham +4
Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an…
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…
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…
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…
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
Sukhdeep S. Sodhi, Ellie Ka-In Chio, Ambarish Jash +18
As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic spee…