57 citations · 84 across the 10 of their papers we have counts for
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Evaluating the Utility of Grounding Documents with Reference-Free LLM-based Metrics
Yilun Hua, Giuseppe Castellucci, Peter Schulam +2
Retrieval Augmented Generation (RAG)'s success depends on the utility the LLM derives from the content used for grounding. Quantifying content utility does not have a definitive sp…
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…
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,…
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…
Evaluation Metrics of Language Generation Models for Synthetic Traffic Generation Tasks
Simone Filice, Jason Ingyu Choi, Giuseppe Castellucci +2
Many Natural Language Generation (NLG) tasks aim to generate a single output text given an input prompt. Other settings require the generation of multiple texts, e.g., for Syntheti…
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…