57 citations · 92 across the 11 of their papers we have counts for
11 papers
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 Product Recommendations for Implicit Superlative Queries
Kaustubh D. Dhole, Nikhita Vedula, Saar Kuzi +3
In Recommender Systems, users often seek the best products through indirect, vague, or under-specified queries, such as "best shoes for trail running". Such queries, also referred…
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…
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks
Sudipta Kar, Giuseppe Castellucci, Simone Filice +2
Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in one model. Training an MTL model requires…
Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance
Anna Gottardi, Osman Ipek, Giuseppe Castellucci +27
Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand…
Almawave-SLU: A new dataset for SLU in Italian
Valentina Bellomaria, Giuseppe Castellucci, Andrea Favalli +1
The widespread use of conversational and question answering systems made it necessary to improve the performances of speaker intent detection and understanding of related semantic…
Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
Giuseppe Castellucci, Valentina Bellomaria, Andrea Favalli +1
Intent Detection and Slot Filling are two pillar tasks in Spoken Natural Language Understanding. Common approaches adopt joint Deep Learning architectures in attention-based recurr…